Spatial-economic analysis of greenhouse gas emissions from agricultural residue burning in Thailand’s rice, maize, and sugarcane cultivation areas

Spatial-economic analysis of greenhouse gas emissions from agricultural residue burning in Thailand’s rice, maize, and sugarcane cultivation areas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial-economic analysis of greenhouse gas emissions from agricultural residue burning in Thailand’s rice, maize, and sugarcane cultivation areas Yingluck Kanchanaroek, Totsanat Rattanakaew, Pidok Kako, Onicha Meangbua, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5924571/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Sustainable Environment Research → Version 1 posted 5 You are reading this latest preprint version Abstract This study investigates the environmental and economic impacts of agricultural residue burning in Thailand, focusing on rice, maize, and sugarcane, which collectively occupy 96.8 million Rai annually and generate 114 million tons of residues. Open burning is a cost-effective but environmentally detrimental practice that contributes significantly to greenhouse gas (GHG) emissions. This research aims to quantify the burned areas, estimate GHG emissions, and assess the Social Cost of Carbon (SCC) using Geographic Information System (GIS) techniques and MODIS satellite imagery combined with bottom-up approach emissions calculations. In addition, the cost of carbon emissions was estimated using the average carbon credit price in Asia as a representative benchmark. The findings reveal annual GHG emissions of approximately 800,000 tons CO ₂ e, primarily from rice (362,231 tons), maize (160,875 tons), and sugarcane (277,314 tons). The SCC is estimated at 146 million Baht, disproportionately affecting the Northern and Central Regions, which exhibit the highest prevalence of burning for rice sugarcane and maize, respectively. This spatial analysis highlights key hot-spots and provides critical insights to inform targeted policy interventions. Its findings emphasize the need for regionally tailored policies to mitigate the environmental and economic costs of open burning. Sustainable alternatives, such as composting are recommended, supported by targeted education, financial incentives, and policy measures. These strategies could substantially reduce emissions, improve air quality, and align Thailand’s agricultural sector with its climate and sustainability goals. GHG Open-burning agricultural waste and Social Cost of Carbon Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Agricultural practices have historically played a pivotal role in shaping economic development, particularly in low- and middle-income countries where they form the backbone of rural economies and livelihoods. The Green Revolution, characterized by the introduction of high-yielding crop varieties, chemical fertilizers, pesticides, and advanced irrigation techniques, significantly transformed agricultural systems globally, promoting food security and stimulating economic growth [1]. In Thailand, these advancements, combined with a policy shift toward commercial crop production, have driven a transition from subsistence farming to large-scale, export-oriented agriculture. This shift has not only enhanced productivity but also increased the cultivation of key cash crops such as rice, maize, and sugarcane, which occupy over 96.8 million rai annually and generate approximately 114 million tons of agricultural residue [2]. However, these gains have come at an environmental cost, particularly through the widespread practice of open burning of agricultural residues, which has emerged as a critical environmental and public health issue [3]. The open burning of agricultural residues involves the deliberate combustion of leftover crop biomass, including stalks, leaves, and straw, typically conducted after harvesting or prior to planting. This practice is widespread across Thailand, where it is primarily used as a low-cost method for field clearance, weed control, and pest management [4]. Despite its perceived convenience, open burning has severe environmental consequences, releasing large quantities of greenhouse gases, including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), all of which are potent contributors to global warming and climate change [5]. Methane, for example, is approximately 25 times more effective at trapping heat in the atmosphere than CO₂ over a 100-year period, making it a particularly damaging component of agricultural emissions [5]. Additionally, incomplete combustion, especially in the case of rice residues in irrigated paddy fields, results in elevated CH₄ emissions, thereby exacerbating Thailand's overall greenhouse gas (GHG) profile [6]. The environmental implications of open burning are compounded by its public health impacts. The emission of particulate matter, particularly PM2.5, is associated with poor air quality and has been linked to respiratory issues and increased morbidity in exposed populations [7]. In Thailand, the burning of rice, maize, and sugarcane residues is a major contributor to air pollution, particularly during the dry season when burning is most intense [3]. The resulting smoke and haze affect not only rural farming communities but also urban areas, highlighting the need for effective residue management strategies. Despite various policy initiatives aimed at mitigating these impacts, agricultural waste management in Thailand remains insufficient. This inadequacy is attributed to weak enforcement of environmental regulations, limited farmer education on sustainable practices, and a lack of financial support for adopting alternative waste disposal methods such as mechanical mulching or bio-energy production [8]. Existing strategies, including incentives for utilizing crop residues in energy production, have not been scaled up effectively, leading to the persistence of open burning as the dominant method of disposal. Evaluating GHG emissions and their associated costs from open burning in Thailand’s key agricultural areas—rice, maize, and sugarcane fields—is crucial for several reasons. First, it allows for a precise quantification of the environmental impacts of this practice, offering a clearer understanding of its contributions to climate change and air quality degradation. Second, such evaluations help pinpoint regional and temporal hot-spots where interventions are most urgently needed, facilitating the development of targeted mitigation strategies. Lastly, comprehensive assessments of GHG emissions from open burning are essential for informing national and international policy commitments, such as Thailand’s obligations under the Paris Agreement and the United Nations Framework Convention on Climate Change [9]. Without accurate and detailed emission inventories, efforts to mitigate climate change will remain inadequate and poorly aligned with broader sustainability goals. In summary, while the economic benefits of Thailand’s agricultural policies and the Green Revolution are evident, the associated environmental and health costs of practices such as open burning cannot be overlooked. Addressing these challenges requires a comprehensive evaluation of GHG emissions from open burning across various crops and regions, alongside the development of more effective and sustainable residue management strategies. 2. Background 2.1 Estimation of GHG emissions The evaluation of GHG emissions from agricultural activities, particularly from the open burning of residues, is critical in understanding their environmental impact. Open burning of agricultural residues in rice, maize, and sugarcane cultivation areas is a major source of GHG emissions. Studies show that in-situ open-burning is a common practice to manage crop residues, especially in developing countries [ 10 , 11 , 12 ]. GHG emissions and pollution from crop residue burning have been estimated to offer insights for policymakers to cope with this leftover biomass sustainably [ 10 ]. For example, in India, crop residue burning (CRB) is considered a common, popular, and cost-effective field preparation, as well as for weed and pest control [ 10 ]. About 24% of crop residue or 117 Mt was burned in situ in 2017 [ 11 ], causing 211 Mt CO 2 e of GHG emissions. China, one of the world's major agricultural countries and the largest producer of rice and wheat, as well as the second-largest producer of corn, also commonly burns crop residue in open fields as a time-saving and convenient disposal method. Around 22.5% of straw from these major crops was burned in situ between 1996 and 2013. In 2013, 155 Mt of this burned straw emitted 193 Mt of GHG [ 13 ]. Likewise, the study [ 14 ] found that 23% or 160 Mt of crop residue from eight major crops was burned in situ, emitting 150, 0.5 and 0.01 Mt of CO 2 , CH 4 and N 2 O, respectively in 2012. In Thailand, 23% of crop residue from rice was left in the open field, of which 30% or 4.54 Mt was burned directly, emitting approximately 5.34 Mt of CO 2 and 44 kt of CH 4 [ 15 ]. Another study in Thailand also found 5.3 Mt of GHG emissions were released from 4.8 Mt of burned rice and sugarcane residue in 2018 [16]. Besides GHG emissions, studies also estimated pollutants from CRB, e.g., Particulate Matter (PM2.5), Elemental Carbon (EC), Organic Carbon (OC) [ 11 ] sulfur dioxide (SO 2 ), nitrogen oxides (NO X ), ammonia (NH 3 ) and non-methane volatile organic compounds (NMVOCs) [ 17 ]. Potential benefits of crop residues are also assessed, which can be converted to environmental and economic impacts. The study [ 11 ] showed that the energy potential of burned crop residue could supply 120 TWh of electricity, accounting for 10% of energy production in India. In addition, a further study [ 18 ] applied carbon credit to evaluate the economic value of avoided GHG emissions from crop residue management. The production of organic fertilizer and biochar with their avoided GHG emission generates − 15 to -90 US $ ton − 1 and 223 to 890 US $ ton − 1 of crop residue depending on crop types. The nutrient content (Nitrogen: N, phosphorus: P, and potassium: K) of crop residue is converted to GHG emissions using the emission factor of chemical fertilizer production [19, 20]. This study [19] showed the life cycle GHG emissions of biochar production to be 550 kg CO 2 e ton − 1 of crop residue, while its application of products: pyrolysis gas, bio-oil, and biochar, avoids 1,470 kg CO 2 e of GHG emissions by offsetting coal-fired power generation and chemical fertilizer consumption and increasing carbon sequestration. The utilization of crop residues through biochar production, combined heat and power generation, and briquette fuel production emits between 0.14–0.2 ton CO 2 e ton − 1 of crop residue during processing, with 0.05–0.08 ton CO 2 e ton − 1 attributed to nutrient loss. These processes also contribute to and cause carbon sinks of 1.75, 0.84, and 1.76 ton CO 2 e ton − 1 , respectively, through soil carbon sequestration and energy displacement [ 20 ]. According to previous works, the bottom-up approach is commonly applied to estimate the amount of GHG emissions. The calculation is straightforward, where the mass of CRB in situ is multiplied by the emission factor [ 10 , 11 , 12 ]. The mass of CRB is determined by such factors as the residue-to-product ratio (RPR), crop production, burned area or the fraction of crop residue burned, dry matter fraction, and burn efficiency. Three main GHGs: CO 2 , CH 4 , and N 2 O, from CRB are calculated based on global warming potentials. Acquiring reliable, domestic, macro-level and crop-specific coefficients can be challenging, creating limitations in having an accurate emission inventory, especially the RPR, emission factor, and the fraction of crop residue burned in the field. With assistance from foreign studies, the use of non-local and nonspecific coefficients allows for estimation, but at the cost of reliability. 2.2. Remote Sensing for Burned Area Assessment Remote sensing (RS) has emerged as a tool for the assessment of burned areas, particularly in agricultural regions. By utilizing satellite-based data, RS offers a non-invasive, large-scale approach for monitoring fire occurrences over time, overcoming the limitations of field-based surveys. This technique is especially effective for evaluating burned areas in rice, maize, and sugarcane cultivation regions thanks to its efficiency, high-resolution imagery, and ability to capture temporal and spatial changes. The use of Moderate Resolution Imaging Spectroradiometer (MODIS) data has been a cornerstone in this field, with several products such as MCD45, MCD64, and FireCCI having proven highly effective in detecting burned areas globally. These products, along with other satellite systems like Landsat-8 and Sentinel-2, offer critical insights into the spatial extent and frequency of agricultural burning [ 21 , 22 ]. RS is the best-suited method for evaluating burned areas due to its extensive coverage, frequent data acquisition, and ability to provide consistent monitoring over large geographic areas. Unlike traditional field surveys, RS enables data collection without physical access to hazardous or remote areas, making it ideal for monitoring fire-prone agricultural regions. Additionally, satellite products like MODIS offer reliable, high-resolution imagery that can be used to track burn patterns and post-fire recovery. For example, the MCD64A1 MODIS Burned Area Monthly Global 500m product, with its high spatial resolution, has been instrumental in providing global assessments of fire-affected areas. This product, designed for large-scale monitoring, offers comprehensive monthly data at a 500-meter grid resolution, making it suitable for assessing agricultural regions where burning is common [ 23 ]. The efficiency of these satellite products in providing real-time, repeatable data also supports their use in long-term environmental monitoring. The application of MODIS satellite data has significantly advanced the assessment of burned areas on a global and regional scale. Giglio, Randerson, and Werf (2013) utilized MODIS to generate a global burned area dataset, revealing an annual average of 348 million hectares of burned land between 1997 and 2011, showcasing the tool’s capacity for large-scale fire detection and its critical role in capturing temporal variability [ 23 ]. Fornacca, Ren, and Xiao (2017) compared multiple MODIS products in China, demonstrating that both MCD45A1 and MCD14ML were the most effective in detecting smaller fires, with producer accuracies reaching 66% for areas larger than 50 hectares [ 24 ]. This underlines the versatility of MODIS in agricultural fire monitoring. Zhang et al. (2021) expanded on this by evaluating the performance of MCD64A1, FireCCI 5.1, and the Copernicus Burnt Area product, with the latter identifying the largest burned areas in agricultural zones, highlighting variation among products in fire detection [ 25 ]. Alencar et al. (2022) further refined these assessments in Brazil by integrating MODIS data with machine learning, enhancing the accuracy of burned area mapping from 1985 to 2020 [26]. These studies collectively emphasize the effectiveness of remote sensing in providing comprehensive, scalable fire monitoring solutions. Remote sensing, particularly MODIS and its related products, is the most efficient and reliable method for assessing burned areas in agricultural regions. Its capacity to capture extensive areas with high temporal and spatial resolution, combined with advanced algorithms, makes it an ideal tool for monitoring fire-related impacts in rice, maize, and sugarcane cultivation zones. Research has consistently demonstrated the accuracy and versatility of RS products in detecting burned areas of various sizes, making this approach indispensable for environmental monitoring and fire management strategies. 3. Methodology The research framework integrates Geographic Information System (GIS) techniques, MODIS satellite imagery, and bottom-up estimation of crop residue resources to evaluate the environmental impacts of open burning in rice, sugarcane, and maize cultivation zones. MODIS data is employed to identify and map burned areas, while the bottom-up estimation combined with Carbon Emission Factors, quantifies the resulting GHG emissions. This framework allows for a comprehensive assessment, linking spatial analysis with emissions estimation, to understand the extent and environmental consequences of agricultural residue burning. 3.1 Mapping of Agricultural Residue Burning in Agricultural Areas The mapping of land use for key crops, such as rice, maize, and sugarcane, during the primary growing season, was conducted at a 1: 25,000 scale for the entire country during the year of the study. The land use map database was updated to this scale, utilizing data from the Land Development Department, Thailand. These land use maps were overlaid with aerial and satellite imagery from different time periods using GIS software. Data sources included land use maps at a scale of 1:4,000 from the Land Development Department, aerial imagery, and medium-to high-resolution satellite images, including LANDSAT, Thaichote satellite images, and Google Earth imagery, recorded during the study year. The updated land use map, at a scale of 1:25,000, was verified through fieldwork and interviews with local officials, community leaders, and residents. Additionally, the analysis of burned areas (hot-spots) in rice, maize, and sugarcane fields was conducted to create a map of burning activities. This was conducted using satellite imagery from the year 2021. This included data from the 2013 administrative boundary database, the economic crop planting map (rice, maize, and sugarcane), and aerial and satellite images from Terra and Aqua satellites. It utilized the MCD64A1.006 MODIS Burned Area Monthly Global 500m product, recorded in 2021,from the Earth Engine Data Catalog via Google Earth Engine. Subsequently, the land use maps for rice, maize, and sugarcane cultivation, at a 1: 25,000 scale, were overlaid with the hot-spot maps from satellite imagery for the study year. The GIS-based analysis was used to identify rice, maize, and sugarcane fields where burning had occurred, distinguishing burned areas specific to these crops from other agricultural lands and forest fire zones. Finally, a comprehensive map was produced showing the agricultural residue burning areas across Thailand’s agricultural landscapes. The data collection process to assess the burned areas of rice, maize, and sugarcane through GIS involved the following steps: Collection of land use maps at a scale of 1:4,000. Gathering of aerial imagery and medium- to high-resolution satellite images, including LANDSAT, Thaichote satellite images, and Google Earth imagery. Compilation of administrative boundary data, with a focus on the 2013 numerical boundaries. Collection of agricultural crop maps for three key economic crops: rice, maize, and sugarcane. Acquisition of aerial imagery and satellite data from the Terra and Aqua satellites, specifically using the MODIS MCD64A1.006 Burned Area Monthly Global 500m dataset recorded in 2021. Use of the Earth Engine Data Catalog via the Google Earth Engine platform for data integration and analysis. 3.2 Greenhouse Gas Emissions Assessment and Analysis We estimated GHG emissions; CO 2 , CH 4 , and N 2 O, from CRB using IPCC 2006 guidelines [ 5 ]. Crop residue burning from the top three residue-producing crops - rice, maize and sugarcane - were selected and accounted for approximately 60% of the total domestic leftover crop residues. Note that, the production of each crop generates various parts of residue, some of which are commonly utilized for goods. Thus, only leftover residues are referred to as crop residue in this study. Crop residues from rice refer to rice straw and stubble, with rice husk being a byproduct of the rice mill. Corn residues refer to leaves, tops, and stalks, while corncobs are a byproduct of maize mill. Sugarcane residues refer to leaves and tops, with bagasse being a byproduct of the sugar mill. Data gathering for the bottom-up estimation of CR resources is mostly official and local-based, consisting of annual crop production acquired from the Office of Agricultural Economics [27] and the Office of the Cane and Sugar Board [ 28 ]. The choice of referred RPR is imperative in assessing the emission inventory, as they vary considerably among references. Especially in the case of rice residues, the value of RPR varied by rice variety, water supply, and harvesting method [ 29 ] and might refer to rice stubble as rice straw in general. These are often considered as equivalent. Local references present the RPR of dry-weight rice straw as 0.5 [30] and 0.81 [ 31 ]. The study's survey [ 32 ] reported the production of rice straw and rice stubble as 357 (58%) and 262 (42%) kg per rai or about 0.74 and 0.54 RPR, respectively, for the rice production of around 3.1 ton per ha [27]. Similarly, the result of 650 kg per rai or about 1.4 RPR of rice straw and stubble together was also found [33]. The study [ 29 ] estimated rice straw’s RPR across 15 rice varieties by collecting samples, with results ranging between 1 and 1.4 or 3.1 and 4.6 ton per ha, accounting for 60% of the total residue. Studies in China cited rice straw’s RPR as 0.9 [ 17 ] and 0.93–1.28 [ 34 ]. A foreign study in Southeast Asia [ 35 ] reports 0.5, 0.7, and 1.4 RPR of rice straw, depending on the cutting height of the stubble (40, 20, and 0 cm respectively). Accordingly, this study applied an average RPR of roughly 0.8 for rice straw from local studies, and that of rice stubble was estimated to be 40% of the total residue, or around 0.5. The cited maize’s RPR of 1 was also an average of study values. The official source [30] presented a value of 1.84 with 42% moisture content or about 1.2 on a dry-weight basis. Local studies used values of 0.82 [ 36 ] and 0.89 [ 37 ] on a dry-weight basis with about 10% moisture content. The study’s survey of energy potential from maize residue presented as 1.1 RPR [ 38 ]. Local references report the sugarcane’s RPR of 0.2 [ 37 ] and 0.17 [30] on a dry-weight basis. Some studies reported values of 0.28 [ 39 ] and 0.24–0.44 [ 40 ], with about 1 and 0.8 kg per m 2 biomass loads respectively. However, according to an average sugarcane production of around 9–12 ton per rai [ 28 ], the RPR of these two studies should be 0.15–0.17 based on the biomass load. Therefore, a sugarcane’s RPR of 0.17 was used. The fraction of dry matter followed the sources of dry-weight RPR which present around 10% of moisture content. A constant value of burn efficiency was used [ 10 ]. The emission factors for the three mentioned GHGs were mostly derived from local literature. Table 1 Coefficients and crops used for the estimation of GHG emission from crop residue burning Table 1 Coefficients and crops used for the estimation of GHG emission from crop residue burning Residue-to-product ratio Dry mater fraction Burn Efficiency Emission factor (g CO 2 e) CO 2 CH 4 N 2 O Rice 1.3 0.9 0.9 1,177 a 4.51 b 0.07 b Corn 1 0.9 0.9 1,350 c,d 4.4 c 0.14 c Sugarcane 0.17 0.9 0.9 1,153 e 3.9 e 0.07 e a [ 41 ], b [42], c [ 14 ], d [ 43 ], e [ 44 ] The burned areas specific to each of crop, derived from the MODIS data, along with the gathered data, are key variables used to estimate the GHG emissions from CRB using the following Eq. (1): E ig = R i × P i × A i × F i × B i × EF ig × p g (1) Where E is the GHG emission (g CO 2 e); i is the crop type (rice, corn, or sugarcane); g is GHG type (CO 2 , CH 4 or N 2 O); R is the residue-to-product ratio; P represents crop production (kg per rai); A is the burned area (rai); F is the fraction of dry matter ; B is the burn efficiency; and EF is the emission factor (g CO 2 e kg − 1 ). p represents global warming potential of the different GHG type (g CO 2 e g − 1 ). Finally, the sum of the three GHG types is the total GHG emissions of different crops in units of g CO 2 e. Although calculating the GHG emissions from the open-burning of crop residues is a key objective of this study, the indirect GHG emissions for nutrient losses were also assessed. Since crop residue is typically mulched, retained or incorporated into the field, without burning or further utilization (such as power generation or briquette production), its nutrients replace the proportion of the chemical fertilizer required. It is important to note that while mulching and soil incorporation may contribute to GHG emissions, their impact is generally insignificant, and tillage is typically a pre-cultivated practice. For the calculation, this indirect effect is simply the multiplication of the amount of crop residue, the nutrient content acquired from the local study [ 45 ] and emission factor of chemical fertilizer manufacturing published by the Thailand Greenhouse Gas Management Organization [ 46 ]. For data gathering, CO 2 emission coefficients or emission factors were gathered from the Thailand Greenhouse Gas Management Organization (TGO) and the Intergovernmental Panel on Climate Change (IPCC). The GHG emissions were assessed in terms of carbon dioxide equivalents (CO 2 e), considering different agricultural residue management methods. CO 2 e represents the primary greenhouse gas emission metric used to evaluate the climate impacts of various practices. Key data for this analysis included: For activities where non-CO 2 greenhouse gases, such as methane (CH 4 ) or sulfur hexafluoride (SF 6 ), were emitted during agricultural residue management, the calculated values were converted into CO 2 e using the Global Warming Potential (GWP) factors for each gas, as specified by the IPCC. The GWP values used for CO 2 , CH 4, and N 2 O, were 1, 21 and 310 respectively [ 5 ]. After estimating CO 2 e for each activity, the economic value of the associated environmental impact was determined by converting the CO 2 e emissions using the carbon credit concept. This was done using the ICAP Allowance Price Explorer, with the European Union Emission Trading Scheme (EU ETS) selected as the reference due to its status as the world's largest carbon market [ 47 ]. 3.3 Social Cost of Carbon This study assesses the Social Cost of Carbon (SCC) associated with GHG emissions from agricultural residues in Thailand. The SCC, a measure reflecting the economic cost of emitting one ton of CO₂e, is calculated by multiplying the carbon emissions (measured in tons of CO₂ equivalents, or ton CO₂e) by the carbon credit price in Thailand, which averaged 34.34 baht per ton CO₂e [ 48 ]. The emissions data for rice, maize, and sugarcane residues across five Regions of Thailand (North, North-East, Central, East, and South) were derived from carbon accounting methodologies that measure the total CO₂e emissions for each crop type, province and region. The study quantifies the economic burden or "cost" of emissions by crop and region, facilitating an understanding of the regional and crop-specific carbon impacts within Thailand’s agricultural sector. 4. Results and Discussion The results of this study demonstrate the significant extent of open burning in agricultural areas across Thailand. Annually, approximately 551,000 rai of rice fields, 175,000 rai of maize fields, and 156,000 rai of sugarcane fields are subjected to open burning practices. These activities contribute to substantial greenhouse gas emissions, with rice field burning releasing an estimated 473,554 tons of CO 2 e, maize fields contributing 223,790 tons, and sugarcane fields adding 360,355 tons. 4.1 Agricultural Residue in Thailand's Agricultural Areas Studies on agricultural residue burning in Thailand show that the highest levels of burning are concentrated in the Northern Region, primarily in rice and maize fields, while sugarcane burning is most prevalent in the Central Region. The following sections provide a detailed analysis by crop. 4.1.1 Rice Cultivation and Residue Burning Rice cultivation in Thailand spans a total area of 68 million rai, predominantly in the North-Eastern Region (60%), followed by the Northern (23%) and Central (12%) Regions. Despite having a smaller cultivation area than the North-Eastern Region, the Northern Region experiences the highest incidence of rice residue burning, accounting for 42% (230,914 rai) of the total 551,727 rai burned annually (Fig. 2 ). This is followed by the Central Region (33%), while no burning is reported in the Southern Region. The provinces most affected by rice field burning include Uttaradit (94,484 rai), Suphan Buri (68,263 rai), and Phra Nakhon Si Ayutthaya (41,481 rai), which together account for significant proportions of the total burned area. This pattern is largely attributed to the off-season rice cultivation prevalent in the Northern and Central Regions, where farmers often grow multiple crops annually, necessitating rapid residue removal between planting cycles [ 4 ]. Approximately 45% of farmers resort to burning as a cost-effectiveness method for clearing fields before and after the harvest, despite the negative environmental impacts. Additionally, 60% of the estimated 117 million tons of rice residues are burned, although only 15% are fully burned due to soil moisture levels, leading to incomplete combustion [ 49 ]. Such practices contribute significantly to air pollution and related health risks, as discussed by Thailand Climate Change Network, which highlights the adverse effects of particulate matter (PM) from rice residue burning on both environmental quality and public health. 4.1.2 Maize Cultivation and Residue Burning Maize is cultivated on 9.67 million rai in Thailand, with the Northern Region accounting for the largest share of both cultivation and burning. Of the total maize fields burned, 74.58% (130,521 rai) are located in the Northern Region, followed by the Central (15.19%) and North-Eastern (9.94%) Regions respectively (Fig. 3 ). The provinces with the highest levels of maize burning include Tak (45,694 rai), Phetchabun (30,996 rai), and Kanchanaburi (15,804 rai). Maize is particularly well-suited to the Northern highlands due to its low water requirements and resilience to sloped terrain. However, the challenges of residue management in these highland areas, especially during the dry season (January to April), lead many farmers to burn crop residues. This practice is favored for its efficiency in controlling weeds and pests, reducing the reliance on chemical inputs, but it exacerbates air quality issues in the Region. According to Thai Publica (2012) and Green News (2021) [ 3 ], maize residue burning is a major contributor to seasonal haze and the associated respiratory problems in Northern Thailand. In contrast, the North-Eastern and Central Regions experience significantly lower levels of maize burning, as seen in provinces such as Loei, Nakhon Ratchasima, and Lopburi, which are characterized by less intensive maize farming practices. 4.1.3 Sugarcane Cultivation and Residue Burning Sugarcane cultivation in Thailand covers 19.25 million rai, with the North-Eastern Region accounting for 49% of the total area, followed by the Central (24%) and Northern (22%) Regions. Of the total 156,362 rai of sugarcane fields burned annually, the Central Region is the most affected, with 50.74% of the burned area, followed by the Northern (19.25%) and North-Eastern (18.7%) Regions (Fig. 4 ). The provinces with the highest levels of sugarcane burning are Suphan Buri (31,959 rai), Lopburi (31,465 rai), and Sa Kaeo (12,684 rai). Sugarcane burning typically occurs in three stages: pre-harvest, post-harvest, and pre-planting. Pre-harvest burning is the most common, primarily driven by labor shortages and the need to reduce harvesting time. However, this practice reduces both the weight and quality of the harvested cane, negatively affecting the overall profitability of the crop [ 50 ]. The expansion of sugarcane cultivation in Thailand has led to increased burning, especially in the Central Region, where large-scale commercial farming predominates. 4.2 Greenhouse Gas Emissions from Agricultural residuals The analysis of GHG emissions from agricultural residues of rice, maize, and sugarcane in Thailand reveals substantial regional and crop-specific differences. The emissions from these residues are measured in ton CO₂e, highlighting how each crop and its respective geographic distribution contribute variously contribute to Thailand's overall agricultural GHG profile (as shown in Table 2 and Fig. 2 – 4 ). Table 2 Carbon Equivalent Emission from rice, maize and sugarcane residuals (unit: ton CO₂e) Province Rice Maize Sugarcane Emission Cost of Carbon Province Emission Cost of Carbon Province Emission Cost of Carbon (ton CO₂ e) (Thai Baht) (ton CO₂ e) (Thai Baht) (ton CO₂e) (Thai Baht) North 151,604 27,743,532 North 119,980 21,956,340 North 53,428 9,777,324 Uttaradit 62,033 11,352,039 Tak 42,004 7,686,732 Phetchabun 20,761 3,799,263 Nakhon Sawan 24,338 4,453,854 Phetchabun 28,493 5,214,219 Nakhon Sawan 10,606 1,940,898 Phitsanulok 18,995 3,476,085 Mae Hong Son 10,624 1,944,192 Kamphaeng Phet 5,943 1,087,569 Phichit 12,578 2,301,774 Chiang Mai 9,370 1,714,710 Uttaradit 5,697 1,042,551 Phrae 10,840 1,983,720 Phrae 8,031 1,469,673 Phitsanulok 5,556 1,016,748 Kamphaeng Phet 6,069 1,110,627 Phitsanulok 5,442 995,886 Sukhothai 3,315 606,645 Phetchabun 4,640 849,120 Nan 3,837 702,171 Tak 873 159,759 Lampang 3,738 684,054 Nakhon Sawan 3,584 655,872 Phichit 498 91,134 Chiang Rai 2,868 524,844 Uttaradit 3,356 614,148 Lampang 179 32,757 Sukhothai 1,505 275,415 Lamphun 2,047 374,601 Other provinces 0 0 Mae Hong Son 1,386 253,638 Lampang 1,868 341,844 Tak 1,110 203,130 Chiang Rai 781 142,923 Nan 701 128,283 Phayao 364 66,612 Chiang Mai 625 114,375 Kamphaeng Phet 77 14,091 Phayao 108 19,764 Phichit 63 11,529 Lamphun 70 12,810 Sukhothai 39 7,137 North-East 78,291 14,327,253 North-East 15,983 2,924,889 North-East 51,802 9,479,766 Kalasin 20,471 3,746,193 Loei 12,318 2,254,194 Chaiyaphum 18,929 3,464,007 Roi Et 14,676 2,685,708 Nakhon- Ratchasima 3,580 655,140 Nakhon- Ratchasima 10,753 1,967,799 Sakon- Nakhon 9,322 1,705,926 Nong Bua Lam Phu 67 12,261 Khon Kaen 5,624 1,029,192 Khon Kaen 9,017 1,650,111 Chaiyaphum 14 2,562 Udon Thani 5,338 976,854 Udon Thani 3,774 690,642 Khon Kaen 2 366 Kalasin 3,460 633,180 Nakhon Phanom 3,596 658,068 Nong Khaii 2 366 Nong Bua Lam Phu 2,447 447,801 Bueng Kan 219 40,077 Other provinces 0 0 Roi Et 2,190 400,770 Maha - Sarakham 3,288 601,704 Sakon- Nakhon 1,162 212,646 Chaiyaphum 3,019 552,477 Maha- Sarakham 775 141,825 Yasothon 2,306 421,998 Loei 599 109,617 Nakhon- Ratchasima 2,002 366,366 Nong- Khaii 183 33,489 Nong Khaii 1,461 267,363 Amnat- Charoen 179 32,757 Ubon Rat- chathani 1,363 249,429 Buri Ram 131 23,973 Surin 1,295 236,985 Nakhon Phanom 21 3,843 Nong Bua Lam Phu 1,009 184,647 Yasothon 11 2,013 Amnat Charoen 1,005 183,915 Other provinces 0 0 Si Sa Ket 214 39,162 Loei 165 30,195 Buri Ram 89 16,287 Mukdahan 0 0 Central 118,522 21,689,526 Central 24,436 4,471,788 Central 140,717 25,751,211 Suphan Buri 44,817 8,201,511 Kanchanaburi 14,528 2,658,624 Suphan Buri 56,681 10,372,623 Ayutthaya 27,234 4,983,822 Lop Buri 7,015 1,283,745 Lop Buri 55,804 10,212,132 Chai Nat 15,513 2,838,879 Uthai Thani 2,062 377,346 Kanchanaburi 12,583 2,302,689 Lop Buri 14,353 2,626,599 Saraburi 623 114,009 Uthai Thani 6,163 1,127,829 Nakhon Nayok 11,400 2,086,200 Suphan Buri 97 17,751 Ratchaburi 4,225 773,175 Nakhon Pathom 1,573 287,859 Ratchaburi 53 9,699 Nakhon Pathom 2,231 408,273 Sing Buri 903 165,249 Chai Nat 33 6,039 Chai Nat 1,263 231,129 Saraburi 605 110,715 Phetchaburi 25 4,575 Phetchaburi 913 167,079 Nonthaburi 573 104,859 Other provinces 0 0 Saraburi 552 101,016 Pathum Thani 483 88,389 Sing Buri 302 55,266 Ang Thong 326 59,658 Other provinces 0 0 Bangkok 175 32,025 Uthai Thani 174 31,842 Phetchaburi 147 26,901 Kanchanaburi 142 25,986 Ratchaburi 104 19,032 Other provinces 0 0 East 13,813 2,527,779 East 478 87,474 East 31,367 5,740,161 Prachin Buri 7,049 1,289,967 Sa Kaeo 439 80,337 Sa Kaeo 22,496 4,116,768 Chachoengsao 5,381 984,723 Chanthaburi 34 6,222 Prachin Buri 5,679 1,039,257 Sa Kaeo 1,086 198,738 Prachin Buri 5 915 Chon Buri 2,403 439,749 Chanthaburi 230 42,090 Other provinces 0 0 Chachoengsao 789 144,387 Chon Buri 67 12,261 Other provinces 0 0 Rayong and Trat 0 0 South (All provinces) 0 0 South (All provinces) 0 0 South (All provinces) 0 0 The Northern Region records the highest emissions from rice residues at 151,604 ton CO₂e, with Uttaradit (62,033 ton CO₂e) and Nakhon Sawan (24,338 ton CO₂e) as major contributors. In the North-Eastern, emissions are comparatively lower, totaling 78,292 ton CO₂e, led by Kalasin and Roi Et. The Central Region ranks second with 118,552 ton CO₂e, primarily from Suphan Buri and Ayutthaya. These results indicate that rice residue emissions are most pronounced in the Northern, likely due to larger cultivation areas and practices favoring residue burning. Maize residue emissions are highest in the Northern Region, with a total of 119,980 ton CO₂e, dominated by Tak and Phetchabun. The North-Eastern contributes only 15,982 ton CO₂e, led by Loei, while the Central Region's maize residue emissions total 24,435 ton CO₂e, mainly from Kanchanaburi and Lop Buri. This pattern suggests that the Northern Region's maize cultivation practices are significantly more emissions-intensive than those in other Regions. Unlike rice and maize, sugarcane residue emissions are highest in the Central Region, reaching 140,716 ton CO₂e, with Suphan Buri and Lop Buri as primary sources. The Northern and North-Eastern Regions produce 53,428 ton CO₂e and 51,803 ton CO₂e, respectively, with Phetchabun and Chaiyaphum as leading emitters. This data underscores that Central Thailand's sugarcane cultivation is highly emissions-intensive due to prevalent residue-burning practices in this Region. GHG emissions from agricultural residues in Thailand are heavily influenced by both regional practices and crop types. The Northern Region, with intensive rice and maize cultivation, shows the highest emissions, primarily due to prevalent open burning methods for residue management. In contrast, the North-Eastern Region, despite cultivating rice, exhibits lower emissions, possibly due to less frequent residue burning. The Central Region, dominated by sugarcane, also has significant emissions from open burning, highlighting the potential impact of a transition to bio-energy or other non-burning practices. These findings underscore the importance of crop- and region-specific strategies for reducing agricultural emissions in Thailand. Table 2 Carbon Equivalent Emission from rice, maize and sugarcane residuals (unit: ton CO₂e) 4.3 Social Cost of Carbon The total SCC for emissions from rice residues was approximately 66 million baht, with the highest SCC in the Northern Region (27,743,607 baht), due to the large scale of rice farming and emissions from open burning practices. For maize, the total SCC was around 29 million baht, with the Northern Region again contributing the largest share (21,956,266 baht) because of intensive cultivation practices. Sugarcane emissions resulted in a total SCC of about 51 million baht, with the Central Region bearing the highest cost (25,751,048 baht), reflecting the prevalence of sugarcane farming in that area and associated residue burning practices. The details are shown in Table 3 and Fig. 5 . Table 3 Cost of Carbon from rice, maize and sugarcane residuals (unit: Thai Baht) Province Rice Province Maize Province Sugarcane North 27,743,532 North 21,956,340 North 9,777,324 Uttaradit 11,352,039 Tak 7,686,732 Phetchabun 3,799,263 Nakhon Sawan 4,453,854 Phetchabun 5,214,219 Nakhon Sawan 1,940,898 Phitsanulok 3,476,085 Mae Hong Son 1,944,192 Kamphaeng Phet 1,087,569 Phichit 2,301,774 Chiang Mai 1,714,710 Uttaradit 1,042,551 Phrae 1,983,720 Phrae 1,469,673 Phitsanulok 1,016,748 Kamphaeng Phet 1,110,627 Phitsanulok 995,886 Sukhothai 606,645 Phetchabun 849,120 Nan 702,171 Tak 159,759 Lampang 684,054 Nakhon Sawan 655,872 Phichit 91,134 Chiang Rai 524,844 Uttaradit 614,148 Lampang 32,757 Sukhothai 275,415 Lamphun 374,601 Other provinces 0 Mae Hong Son 253,638 Lampang 341,844 Tak 203,130 Chiang Rai 142,923 Nan 128,283 Phayao 66,612 Chiang Mai 114,375 Kamphaeng Phet 14,091 Phayao 19,764 Phichit 11,529 Lamphun 12,810 Sukhothai 7,137 North-East 14,327,253 North-East 2,924,889 North-East 9,479,766 Kalasin 3,746,193 Loei 2,254,194 Chaiyaphum 3,464,007 Roi Et 2,685,708 Nakhon Ratchasima 655,140 Nakhon Ratchasima 1,967,799 Sakon Nakhon 1,705,926 Nong Bua Lam Phu 12,261 Khon Kaen 1,029,192 Khon Kaen 1,650,111 Chaiyaphum 2,562 Udon Thani 976,854 Udon Thani 690,642 Khon Kaen 366 Kalasin 633,180 Nakhon Phanom 658,068 Nong Khaii 366 Nong Bua Lam Phu 447,801 Bueng Kan 40,077 Other provinces 0 Roi Et 400,770 Maha Sarakham 601,704 Sakon Nakhon 212,646 Chaiyaphum 552,477 Maha Sarakham 141,825 Yasothon 421,998 Loei 109,617 Nakhon Ratchasima 366,366 Nong Khaii 33,489 Nong Khaii 267,363 Amnat Charoen 32,757 Ubon Ratchathani 249,429 Buri Ram 23,973 Surin 236,985 Nakhon Phanom 3,843 Nong Bua Lam Phu 184,647 Yasothon 2,013 Amnat Charoen 183,915 Other provinces 0 Si Sa Ket 39,162 Loei 30,195 Buri Ram 16,287 Mukdahan 0 Central 21,689,526 Central 4,471,788 Central 25,751,211 Suphan Buri 8,201,511 Kanchanaburi 2,658,624 Suphan Buri 10,372,623 Ayutthaya 4,983,822 Lop Buri 1,283,745 Lop Buri 10,212,132 Chai Nat 2,838,879 Uthai Thani 377,346 Kanchanaburi 2,302,689 Lop Buri 2,626,599 Saraburi 114,009 Uthai Thani 1,127,829 Nakhon Nayok 2,086,200 Suphan Buri 17,751 Ratchaburi 773,175 Nakhon Pathom 287,859 Ratchaburi 9,699 Nakhon Pathom 408,273 Sing Buri 165,249 Chai Nat 6,039 Chai Nat 231,129 Saraburi 110,715 Phetchaburi 4,575 Phetchaburi 167,079 Nonthaburi 104,859 Other provinces 0 Saraburi 101,016 Pathum Thani 88,389 Sing Buri 55,266 Ang Thong 59,658 Other provinces 0 Bangkok 32,025 Uthai Thani 31,842 Phetchaburi 26,901 Kanchanaburi 25,986 Ratchaburi 19,032 Other provinces 0 East 2,527,779 East 87,474 East 5,740,161 Prachin Buri 1,289,967 Sa Kaeo 80,337 Sa Kaeo 4,116,768 Chachoengsao 984,723 Chanthaburi 6,222 Prachin Buri 1,039,257 Sa Kaeo 198,738 Prachin Buri 915 Chon Buri 439,749 Chanthaburi 42,090 Other provinces 0 Chachoengsao 144,387 Chon Buri 12,261 Other provinces 0 Rayong and Trat 0 South (All provinces) 0 South (All provinces) 0 South (All provinces) 0 Table 3 Cost of Carbon from rice, maize and sugarcane residuals (unit: Thai Baht) The SCC findings highlight substantial social costs attributed to regional agricultural practices, with rice and maize in the Northern and sugarcane in the Central Regions as the largest contributors to carbon costs. These insights underscore the economic implications of residue management practices and point to potential cost savings through sustainable residue management alternatives, particularly in high-emission areas. Table 4 Summary of Cultivated and Burned Areas of Rice, Maize, and Sugarcane: Greenhouse Gas Emissions and Social Cost of Carbon from Crop Residues across the Regions Table 4 Summary of Cultivated and Burned Areas of Rice, Maize, and Sugarcane: Greenhouse Gas Emissions and Social Cost of Carbon from Crop Residues across the Regions Regions Cultivated Area (Rai) Burned Area (Rai) Carbon Emission (ton CO 2 e) Social Cost of Carbon (Thai Baht) Rice North 15,360,965 230,914 151,604 27,743,607 North-East 40,939,421 119,249 78,292 14,327,400 Central 8,282,695 180,525 118,522 21,689,523 East 2,304,528 21,039 13,813 2,527,771 South 1,204,684 - - - Total 68,092,293 551,727 362,231 66,288,301 Maize North 7,586,619 130,521 119,980 21,956,266 North-East 1,362,818 17,386 15,982 2,924,676 Central 680,124 26,582 24,435 4,471,629 East 43,366 520 478 87,474 South 895 - - - Total 9,673,822 175,009 160,875 29,440,045 Sugarcane North 4,243,030 30,125 53,428 9,777,297 North-East 9,467,956 29,209 51,803 9,480,003 Central 4,630,232 79,342 140,716 25,751,048 East 905,607 17,686 31,367 5,740,125 South 880 - - - Total 19,247,705 156,362 277,314 50,748,473 Thailand in Total 97,013,820 883,098 800,420 146,476,820 This comprehensive analysis presents (as shown in Table 4 ) cultivated and burned areas for rice, maize, and sugarcane across different Regions of Thailand, detailing their associated carbon emissions and the social costs of carbon. The data reveals that rice cultivation occupies the largest area (68,092,293 rai), with 551,727 rai burned, generating 362,231 ton CO 2 e and incurring a social carbon cost of 66,288,301 Thai Baht. Across all three crops, Thailand's total cultivated area reaches 97,013,820 rai, with 883,098 rai burned, producing 800,420 ton CO 2 e and a total social carbon cost of 146,476,820 Thai Baht. 5. Policy Agricultural residue burning is a significant contributor to GHG emissions globally, yet detailed, region-specific analyses are often lacking, particularly in Southeast Asia. Previous studies have extensively documented the environmental and economic impacts of open burning [ 51 , 12 ]. However, there remains a knowledge gap concerning the integration of advanced remote sensing technologies with detailed emissions inventories and economic assessments, particularly in Thailand's diverse agricultural landscapes. This study bridges these gaps by providing a comprehensive analysis of burned areas, GHG emissions, and associated social costs for rice, maize, and sugarcane residues. The findings indicate substantial environmental impacts from open burning, with total emissions of approximately 800,000 tons CO₂e annually, primarily concentrated in rice (473,554 tons CO₂e), maize (223,790 tons CO₂e), and sugarcane (360,355 tons CO₂e). Spatial analysis revealed the Northern Region as the hotspot for rice and maize burning, while the Central Region exhibited the highest prevalence of sugarcane burning. These emissions translate to a significant economic burden, with a calculated SCC of approximately 146 million baht annually. This spatial analysis could serve as a basis for targeting pilot areas for policy-specific measurement. The emission patterns observed align with studies in China and India, where crop residue burning remains a prevalent practice due to its low cost and efficiency [ 12 , 11 ]. However, the emissions per hectare in Thailand are relatively higher, potentially due to differences in residue-to-product ratios and combustion efficiencies. The SCC findings also underscore the disproportionate burden borne by specific regions, emphasizing the need for regionally targeted interventions. Similar trends were observed in India, where SCC varied widely across states [ 51 ]. However, the study also highlights Thailand's potential to lead in adopting innovative residue management techniques, building on regional research in bio-energy and carbon sequestration [19]. Challenges in enforcing residue management policies stem from limited financial support and awareness among farmers. Addressing these gaps requires targeted education and subsidy programs to incentivize sustainable practices. Policymakers should prioritize funding for localized pilot projects on biochar and composting, coupled with assessments of their long-term environmental and economic benefits. Establishing carbon markets specific to agricultural residues could also drive innovation and investment. A multi-faceted approach combining regulatory enforcement, economic incentives, and farmer education is essential to transition Thailand's agricultural sector towards sustainability. Collaboration between government agencies, researchers, and farming communities will be critical in achieving these goals. 6. Conclusion This study underscores the critical environmental challenges posed by open burning practices in Thailand's rice, maize, and sugarcane cultivation areas, particularly in the Northern and Central Regions. The substantial greenhouse gas emissions linked to these practices, estimated at approximately one million tons CO 2 e annually, highlight the urgent need for effective mitigation strategies. The analysis reveals that open-burning, while prevalent due to its cost-effectiveness, significantly undermines the nation's climate objectives and public health. Therefore, it is imperative to implement regionally targeted policy measures that promote sustainable alternatives to open burning, such as composting and biomass utilization. By focusing on high-emission crops and areas, policymakers can foster a transition towards environmentally friendly agricultural practices, enhancing air quality and contributing to Thailand's broader climate goals. Collaborative efforts among government agencies, researchers, and farming communities will be essential in driving these changes and achieving meaningful reductions in agricultural emissions. The findings of this study not only call for immediate action but also provide a roadmap for sustainable agricultural management that aligns with ecological and economic sustainability. Declarations Availability of data and materials All data generated or analyzed during this study are available on a request from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by the Thailand Research Fund under grant number N21A650033 through the Office of Agricultural Policy Research, Knowledge Network Institute of Thailand. Authors' contributions Yingluck Kanchanaroek led the study's conceptualization and coordinated the project administration. She conducted the social cost analysis and played a central role in original draft manuscript writing and editing. Totsanat Rattanakaew was responsible for the agricultural land use and burned area analysis, spatial mapping, and editing of the Spatial methodology section. Pidok Kako conducted the greenhouse gas emission analysis and edited related sections of the manuscript. Onicha Meangbua curated social cost data, managed the references, and handled formatting. Warayut Doungjun was responsible for data visualization and agricultural residue data curation. All authors contributed to framing, reviewing, and finalizing the manuscript and approved the final version. This paper involves secondary analysis of agricultural and environmental data and does not involve human or animal subjects. While we utilized some spatial data from a previously approved research project (Ethics Review Committee for Human Research, Thammasat University (Social Science), Research Project Code 012/2565, Certificate of Approval No. 007/2565), the current paper represents a substantial extension and further development of that original work. Acknowledgement The authors wish to thank the College of Interdisciplinary Studies, Thammasat University References Evenson, R., & Gollin, D. Assessing the Impact of the Green Revolution, 1960 to 2000. Science, 2003;758-762. Office of Agricultural Economics (OAE). Area of agricultural use. 2021. https://www.oae.go.th/view/1/ตารางแสดงรายละเอียดข้าวนาปี/TH-TH. Accessed Oct 2022. 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Net Energy Ratio and Life cycle greenhouse gases (GHG) assessment of bio-dimethyl ether (DME) produced from various agricultural residues in Thailand. Journal of Clean Production. 2016;134:523-531. Kumnungphon Yinninta, Chichana Tanat, Sukkasam Natnicha, Tararuk Churat. Briquette fuel potential evolution from maize residual in plantation area: case study in northern part of Thailand. Journal of renewable energy for community. 2019;32-36. Kanittha Kanokkanjana, Savitri Garivait. Estimation of Emission from Open Burning of Sugarcane Residues before Harvesting. GMSARN International Journal. 2012;6:157–162. Wilaiwan Sornpoon, Sébastien Bonnet, Poonpipope Kasemsap, Praphan Prasertsak, Savitri Garivait. Estimation of emissions from sugarcane field burning in thailand using bottom-up country-specific activity data. Atmosphere. 2014;5:669-985. Jirataya Pansuk, Agapol Junpen, Savitri Garivai. Assessment of Air Pollution from Household Solid Waste Open Burning in Thailand. Sustainability. 2018;10(7):2-16. Ryan R. Romasanta, Bjoern Ole Sander, Yam Kanta Gaihre, Ma. Carmelita Alberto, Martin Gummert , James Quilty, Van Hung Nguyen, Angeli Grace Castalone, Carlito Balingbing, Joseph Sandro, Teodoro Correa Jr, Reiner Wassmann. How does burning of rice straw affect CH4 and N2O emissions? A comparative experiment of different on-field straw management practices. Agriculture, Ecosystems & Environment. 2017;239:143-153. Haiyan Ni, Yongming Han, Junji Cao, L.-W. Antony Chen, Jie Tian, Xiaoliang Wang, Judith C. Chow, John G. Watson, Qiyuan Wang, Ping Wang, Hua Li, Ru-Jin Huang. Emission characteristics of carbonaceous particles and trace gases from open burning of crop residues in China. Asmospheric Environment. 2015;123:399-406. Agapol Junpen, Jirataya Pansuk, Savitri Garivai. Emission of reduced air emissions as a result of the implementation of the measure to reduce burned sugarcane in Thailand. Astmosphere. 2020. Sailim Sorot. Organic Fertilizer and Utilization in Thailand: Land Development Department: http://www1.ldd.go.th/WEB_PSD/PDF/expert%20work/3.pdf; 2016. TGO. Carbon Footprint of Product: Thailand Greenhouse Gas Management Organization; 2024. European Commission. Development of EU ETS (2005-2020). 2024. TGO. Carbon Credit: Thailand Greenhouse Gas Management Organization; 2022. TCCN. Burning rice waste in rice fields has adverse health effects: Thailand Climate Change Network. https://www.tccnclimate.com/; 2021. R.C Muchow, A.J Higgins, A.V Rudd, A.W Ford. Optimising harvest date in sugar production: a case study for the Mossman mill region in Australia: II. Sensitivity to crop age and crop class distribution. Field Crops Research. 1998;57(3):243-251. Monish Vijay Deshpande, Nitish Kumar, Dhanyalekshmi Pillai, Vijesh V. Krishna, Meha Jain. Greenhouse gas emissions from agricultural residue burning have increased by 75 % since 2011 across India. Science of The Total Environment. 2023;904:1-14. Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Sustainable Environment Research → Version 1 posted Reviewers agreed at journal 20 Mar, 2025 Reviewers invited by journal 18 Mar, 2025 Editor assigned by journal 17 Mar, 2025 First submitted to journal 15 Mar, 2025 Editorial decision: Major revision 13 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5924571","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430739639,"identity":"fc3178f8-06e0-480f-b94f-3934169d3b72","order_by":0,"name":"Yingluck Kanchanaroek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACZiB+wJBgwM/AwEaClgSgFskGorUwQLUYHCBWi8Fx3sMvEtvSjI1vJD978KGCQZ5f7AABLYf50iwS23LMzG6kmRvOOMNgOHN2AiEtPGYGiW0VNmY3EsykeduATrxNrBbjGenfiNZi/ADkMAOJHCJtkQTawpBwLs1Y4sybMskZZyQI+4Xv/BnjDx/Kkg3729O3SXyosJHnlyagRQEYHRJglgBYpQR+5SAg38DA/AHM4j9AWPUoGAWjYBSMTAAAXn5Cg8A+KY8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7430-3579","institution":"Thammasat University","correspondingAuthor":true,"prefix":"","firstName":"Yingluck","middleName":"","lastName":"Kanchanaroek","suffix":""},{"id":430739640,"identity":"d7f29541-13ff-4d4a-bc42-311d0e201583","order_by":1,"name":"Totsanat Rattanakaew","email":"","orcid":"","institution":"Land Development 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13:28:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5924571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5924571/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42834-025-00261-1","type":"published","date":"2025-10-14T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78915435,"identity":"acf50eb9-a876-42d9-bb47-dd84b2929c03","added_by":"auto","created_at":"2025-03-20 18:23:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":807996,"visible":true,"origin":"","legend":"\u003cp\u003eFramework\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5924571/v1/2430c2c24cdb0ba19565c7d0.jpg"},{"id":78915436,"identity":"c962f1f2-d5c9-4c4c-b458-ce9062d4e39f","added_by":"auto","created_at":"2025-03-20 18:23:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167400,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of (Left) rice residue burned areas and its effect on (Right) GHG emission\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5924571/v1/c888b3be43c29160f119ee3d.jpg"},{"id":78915878,"identity":"ec3c0356-4b9e-49f7-9683-13f3c199aeff","added_by":"auto","created_at":"2025-03-20 18:31:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166373,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of (Left) maize residue burned areas and its effect on (Right) GHG emission\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5924571/v1/543886bbeb81f7ae094f0c5e.jpg"},{"id":78914958,"identity":"4e972e1c-2502-4c96-8ab0-b10e825c9269","added_by":"auto","created_at":"2025-03-20 18:15:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":173617,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of (Left) sugarcane residue burned areas and its effect on (Right) GHG emission\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5924571/v1/d0efec88610db505d20d633c.jpg"},{"id":78914964,"identity":"9b08b09f-dc01-41ee-9a22-85c7e76952c9","added_by":"auto","created_at":"2025-03-20 18:15:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":160266,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution of the Cost of Carbon of (Left) Rice, (Middle) Maize and (Right) Sugarcane\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5924571/v1/da8eb4165ff2ac1f72e23f18.jpg"},{"id":93956854,"identity":"6f28bd3b-a196-42c0-8775-3965d1c87f34","added_by":"auto","created_at":"2025-10-20 16:12:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3451427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5924571/v1/c408f4b9-5553-44ad-b976-20788c12a672.pdf"}],"financialInterests":"","formattedTitle":"Spatial-economic analysis of greenhouse gas emissions from agricultural residue burning in Thailand’s rice, maize, and sugarcane cultivation areas","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgricultural practices have historically played a pivotal role in shaping economic development, particularly in low- and middle-income countries where they form the backbone of rural economies and livelihoods. The Green Revolution, characterized by the introduction of high-yielding crop varieties, chemical fertilizers, pesticides, and advanced irrigation techniques, significantly transformed agricultural systems globally, promoting food security and stimulating economic growth [1]. In Thailand, these advancements, combined with a policy shift toward commercial crop production, have driven a transition from subsistence farming to large-scale, export-oriented agriculture. This shift has not only enhanced productivity but also increased the cultivation of key cash crops such as rice, maize, and sugarcane, which occupy over 96.8 million rai annually and generate approximately 114 million tons of agricultural residue [2]. However, these gains have come at an environmental cost, particularly through the widespread practice of open burning of agricultural residues, which has emerged as a critical environmental and public health issue [3].\u003c/p\u003e\n\u003cp\u003eThe open burning of agricultural residues involves the deliberate combustion of leftover crop biomass, including stalks, leaves, and straw, typically conducted after harvesting or prior to planting. \u0026nbsp;This practice is widespread across Thailand, where it is primarily used as a low-cost method for field clearance, weed control, and pest management [4].\u0026nbsp;Despite its perceived convenience, open burning has severe environmental consequences, releasing large quantities of greenhouse gases, including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), all of which are potent contributors to global warming and climate change\u0026nbsp;[5]. Methane, for example, is approximately 25 times more effective at trapping heat in the atmosphere than CO₂ over a 100-year period, making it a particularly damaging component of agricultural emissions\u0026nbsp;[5]. Additionally, incomplete combustion, especially in the case of rice residues in irrigated paddy fields, results in elevated CH₄ emissions, thereby exacerbating Thailand's overall greenhouse gas (GHG) profile\u0026nbsp;[6].\u003c/p\u003e\n\u003cp\u003eThe environmental implications of open burning are compounded by its public health impacts. The emission of particulate matter, particularly PM2.5, is associated with poor air quality and has been linked to respiratory issues and increased morbidity in exposed populations [7]. In Thailand, the burning of rice, maize, and sugarcane residues is a major contributor to air pollution, particularly during the dry season when burning is most intense\u0026nbsp;[3]. The resulting smoke and haze affect not only rural farming communities but also urban areas, highlighting the need for effective residue management strategies.\u003c/p\u003e\n\u003cp\u003eDespite various policy initiatives aimed at mitigating these impacts, agricultural waste management in Thailand remains insufficient. This inadequacy is attributed to weak enforcement of environmental regulations, limited farmer education on sustainable practices, and a lack of financial support for adopting alternative waste disposal methods such as mechanical mulching or bio-energy production [8]. Existing strategies, including incentives for utilizing crop residues in energy production, have not been scaled up effectively, leading to the persistence of open burning as the dominant method of disposal.\u003c/p\u003e\n\u003cp\u003eEvaluating GHG emissions and their associated costs from open burning in Thailand’s key agricultural areas—rice, maize, and sugarcane fields—is crucial for several reasons. First, it allows for a precise quantification of the environmental impacts of this practice, offering a clearer understanding of its contributions to climate change and air quality degradation. Second, such evaluations help pinpoint regional and temporal hot-spots where interventions are most urgently needed, facilitating the development of targeted mitigation strategies. Lastly, comprehensive assessments of GHG emissions from open burning are essential for informing national and international policy commitments, such as Thailand’s obligations under the Paris Agreement and the United Nations Framework Convention on Climate Change [9]. Without accurate and detailed emission inventories, efforts to mitigate climate change will remain inadequate and poorly aligned with broader sustainability goals.\u003c/p\u003e\n\u003cp\u003eIn summary, while the economic benefits of Thailand’s agricultural policies and the Green Revolution are evident, the associated environmental and health costs of practices such as open burning cannot be overlooked. Addressing these challenges requires a comprehensive evaluation of GHG emissions from open burning across various crops and regions, alongside the development of more effective and sustainable residue management strategies.\u003c/p\u003e"},{"header":"2. Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Estimation of GHG emissions\u003c/h2\u003e \u003cp\u003eThe evaluation of GHG emissions from agricultural activities, particularly from the open burning of residues, is critical in understanding their environmental impact. Open burning of agricultural residues in rice, maize, and sugarcane cultivation areas is a major source of GHG emissions.\u003c/p\u003e \u003cp\u003eStudies show that in-situ open-burning is a common practice to manage crop residues, especially in developing countries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. GHG emissions and pollution from crop residue burning have been estimated to offer insights for policymakers to cope with this leftover biomass sustainably [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For example, in India, crop residue burning (CRB) is considered a common, popular, and cost-effective field preparation, as well as for weed and pest control [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. About 24% of crop residue or 117 Mt was burned in situ in 2017 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e], causing 211 Mt CO\u003csub\u003e2\u003c/sub\u003ee of GHG emissions. China, one of the world's major agricultural countries and the largest producer of rice and wheat, as well as the second-largest producer of corn, also commonly burns crop residue in open fields as a time-saving and convenient disposal method. Around 22.5% of straw from these major crops was burned in situ between 1996 and 2013. In 2013, 155 Mt of this burned straw emitted 193 Mt of GHG [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Likewise, the study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] found that 23% or 160 Mt of crop residue from eight major crops was burned in situ, emitting 150, 0.5 and 0.01 Mt of CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO, respectively in 2012. In Thailand, 23% of crop residue from rice was left in the open field, of which 30% or 4.54 Mt was burned directly, emitting approximately 5.34 Mt of CO\u003csub\u003e2\u003c/sub\u003e and 44 kt of CH\u003csub\u003e4\u003c/sub\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Another study in Thailand also found 5.3 Mt of GHG emissions were released from 4.8 Mt of burned rice and sugarcane residue in 2018 [16].\u003c/p\u003e \u003cp\u003eBesides GHG emissions, studies also estimated pollutants from CRB, e.g., Particulate Matter (PM2.5), Elemental Carbon (EC), Organic Carbon (OC) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e] sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), nitrogen oxides (NO\u003csub\u003eX\u003c/sub\u003e), ammonia (NH\u003csub\u003e3\u003c/sub\u003e) and non-methane volatile organic compounds (NMVOCs) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Potential benefits of crop residues are also assessed, which can be converted to environmental and economic impacts. The study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e] showed that the energy potential of burned crop residue could supply 120 TWh of electricity, accounting for 10% of energy production in India. In addition, a further study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e18\u003c/span\u003e] applied carbon credit to evaluate the economic value of avoided GHG emissions from crop residue management. The production of organic fertilizer and biochar with their avoided GHG emission generates \u0026minus;\u0026thinsp;15 to -90 US\u003cspan\u003e$\u003c/span\u003e ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 223 to 890 US\u003cspan\u003e$\u003c/span\u003e ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of crop residue depending on crop types. The nutrient content (Nitrogen: N, phosphorus: P, and potassium: K) of crop residue is converted to GHG emissions using the emission factor of chemical fertilizer production [19, 20]. This study [19] showed the life cycle GHG emissions of biochar production to be 550 kg CO\u003csub\u003e2\u003c/sub\u003ee ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of crop residue, while its application of products: pyrolysis gas, bio-oil, and biochar, avoids 1,470 kg CO\u003csub\u003e2\u003c/sub\u003ee of GHG emissions by offsetting coal-fired power generation and chemical fertilizer consumption and increasing carbon sequestration. The utilization of crop residues through biochar production, combined heat and power generation, and briquette fuel production emits between 0.14\u0026ndash;0.2 ton CO\u003csub\u003e2\u003c/sub\u003ee ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of crop residue during processing, with 0.05\u0026ndash;0.08 ton CO\u003csub\u003e2\u003c/sub\u003ee ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e attributed to nutrient loss. These processes also contribute to and cause carbon sinks of 1.75, 0.84, and 1.76 ton CO\u003csub\u003e2\u003c/sub\u003ee ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, through soil carbon sequestration and energy displacement [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to previous works, the bottom-up approach is commonly applied to estimate the amount of GHG emissions. The calculation is straightforward, where the mass of CRB in situ is multiplied by the emission factor [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The mass of CRB is determined by such factors as the residue-to-product ratio (RPR), crop production, burned area or the fraction of crop residue burned, dry matter fraction, and burn efficiency. Three main GHGs: CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, and N\u003csub\u003e2\u003c/sub\u003eO, from CRB are calculated based on global warming potentials. Acquiring reliable, domestic, macro-level and crop-specific coefficients can be challenging, creating limitations in having an accurate emission inventory, especially the RPR, emission factor, and the fraction of crop residue burned in the field. With assistance from foreign studies, the use of non-local and nonspecific coefficients allows for estimation, but at the cost of reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Remote Sensing for Burned Area Assessment\u003c/h2\u003e \u003cp\u003eRemote sensing (RS) has emerged as a tool for the assessment of burned areas, particularly in agricultural regions. By utilizing satellite-based data, RS offers a non-invasive, large-scale approach for monitoring fire occurrences over time, overcoming the limitations of field-based surveys. This technique is especially effective for evaluating burned areas in rice, maize, and sugarcane cultivation regions thanks to its efficiency, high-resolution imagery, and ability to capture temporal and spatial changes.\u003c/p\u003e \u003cp\u003eThe use of Moderate Resolution Imaging Spectroradiometer (MODIS) data has been a cornerstone in this field, with several products such as MCD45, MCD64, and FireCCI having proven highly effective in detecting burned areas globally. These products, along with other satellite systems like Landsat-8 and Sentinel-2, offer critical insights into the spatial extent and frequency of agricultural burning [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRS is the best-suited method for evaluating burned areas due to its extensive coverage, frequent data acquisition, and ability to provide consistent monitoring over large geographic areas. Unlike traditional field surveys, RS enables data collection without physical access to hazardous or remote areas, making it ideal for monitoring fire-prone agricultural regions. Additionally, satellite products like MODIS offer reliable, high-resolution imagery that can be used to track burn patterns and post-fire recovery. For example, the MCD64A1 MODIS Burned Area Monthly Global 500m product, with its high spatial resolution, has been instrumental in providing global assessments of fire-affected areas. This product, designed for large-scale monitoring, offers comprehensive monthly data at a 500-meter grid resolution, making it suitable for assessing agricultural regions where burning is common [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The efficiency of these satellite products in providing real-time, repeatable data also supports their use in long-term environmental monitoring.\u003c/p\u003e \u003cp\u003eThe application of MODIS satellite data has significantly advanced the assessment of burned areas on a global and regional scale. Giglio, Randerson, and Werf (2013) utilized MODIS to generate a global burned area dataset, revealing an annual average of 348\u0026nbsp;million hectares of burned land between 1997 and 2011, showcasing the tool\u0026rsquo;s capacity for large-scale fire detection and its critical role in capturing temporal variability [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Fornacca, Ren, and Xiao (2017) compared multiple MODIS products in China, demonstrating that both MCD45A1 and MCD14ML were the most effective in detecting smaller fires, with producer accuracies reaching 66% for areas larger than 50 hectares [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This underlines the versatility of MODIS in agricultural fire monitoring. Zhang et al. (2021) expanded on this by evaluating the performance of MCD64A1, FireCCI 5.1, and the Copernicus Burnt Area product, with the latter identifying the largest burned areas in agricultural zones, highlighting variation among products in fire detection [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Alencar et al. (2022) further refined these assessments in Brazil by integrating MODIS data with machine learning, enhancing the accuracy of burned area mapping from 1985 to 2020 [26]. These studies collectively emphasize the effectiveness of remote sensing in providing comprehensive, scalable fire monitoring solutions.\u003c/p\u003e \u003cp\u003eRemote sensing, particularly MODIS and its related products, is the most efficient and reliable method for assessing burned areas in agricultural regions. Its capacity to capture extensive areas with high temporal and spatial resolution, combined with advanced algorithms, makes it an ideal tool for monitoring fire-related impacts in rice, maize, and sugarcane cultivation zones. Research has consistently demonstrated the accuracy and versatility of RS products in detecting burned areas of various sizes, making this approach indispensable for environmental monitoring and fire management strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe research framework integrates Geographic Information System (GIS) techniques, MODIS satellite imagery, and bottom-up estimation of crop residue resources to evaluate the environmental impacts of open burning in rice, sugarcane, and maize cultivation zones. MODIS data is employed to identify and map burned areas, while the bottom-up estimation combined with Carbon Emission Factors, quantifies the resulting GHG emissions. This framework allows for a comprehensive assessment, linking spatial analysis with emissions estimation, to understand the extent and environmental consequences of agricultural residue burning.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Mapping of Agricultural Residue Burning in Agricultural Areas\u003c/h2\u003e \u003cp\u003eThe mapping of land use for key crops, such as rice, maize, and sugarcane, during the primary growing season, was conducted at a 1: 25,000 scale for the entire country during the year of the study. The land use map database was updated to this scale, utilizing data from the Land Development Department, Thailand. These land use maps were overlaid with aerial and satellite imagery from different time periods using GIS software. Data sources included land use maps at a scale of 1:4,000 from the Land Development Department, aerial imagery, and medium-to high-resolution satellite images, including LANDSAT, Thaichote satellite images, and Google Earth imagery, recorded during the study year.\u003c/p\u003e \u003cp\u003eThe updated land use map, at a scale of 1:25,000, was verified through fieldwork and interviews with local officials, community leaders, and residents. Additionally, the analysis of burned areas (hot-spots) in rice, maize, and sugarcane fields was conducted to create a map of burning activities. This was conducted using satellite imagery from the year 2021. This included data from the 2013 administrative boundary database, the economic crop planting map (rice, maize, and sugarcane), and aerial and satellite images from Terra and Aqua satellites. It utilized the MCD64A1.006 MODIS Burned Area Monthly Global 500m product, recorded in 2021,from the Earth Engine Data Catalog via Google Earth Engine.\u003c/p\u003e \u003cp\u003eSubsequently, the land use maps for rice, maize, and sugarcane cultivation, at a\u003c/p\u003e \u003cp\u003e1: 25,000 scale, were overlaid with the hot-spot maps from satellite imagery for the study year. The GIS-based analysis was used to identify rice, maize, and sugarcane fields where burning had occurred, distinguishing burned areas specific to these crops from other agricultural lands and forest fire zones. Finally, a comprehensive map was produced showing the agricultural residue burning areas across Thailand\u0026rsquo;s agricultural landscapes.\u003c/p\u003e \u003cp\u003eThe data collection process to assess the burned areas of rice, maize, and sugarcane through GIS involved the following steps:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCollection of land use maps at a scale of 1:4,000.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGathering of aerial imagery and medium- to high-resolution satellite images, including LANDSAT, Thaichote satellite images, and Google Earth imagery.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCompilation of administrative boundary data, with a focus on the 2013 numerical boundaries.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCollection of agricultural crop maps for three key economic crops: rice, maize, and sugarcane.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAcquisition of aerial imagery and satellite data from the Terra and Aqua satellites, specifically using the MODIS MCD64A1.006 Burned Area Monthly Global 500m dataset recorded in 2021.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUse of the Earth Engine Data Catalog via the Google Earth Engine platform for data integration and analysis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Greenhouse Gas Emissions Assessment and Analysis\u003c/h2\u003e \u003cp\u003eWe estimated GHG emissions; CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, and N\u003csub\u003e2\u003c/sub\u003eO, from CRB using IPCC 2006 guidelines [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Crop residue burning from the top three residue-producing crops - rice, maize and sugarcane - were selected and accounted for approximately 60% of the total domestic leftover crop residues. Note that, the production of each crop generates various parts of residue, some of which are commonly utilized for goods. Thus, only leftover residues are referred to as crop residue in this study. Crop residues from rice refer to rice straw and stubble, with rice husk being a byproduct of the rice mill. Corn residues refer to leaves, tops, and stalks, while corncobs are a byproduct of maize mill. Sugarcane residues refer to leaves and tops, with bagasse being a byproduct of the sugar mill.\u003c/p\u003e \u003cp\u003eData gathering for the bottom-up estimation of CR resources is mostly official and local-based, consisting of annual crop production acquired from the Office of Agricultural Economics [27] and the Office of the Cane and Sugar Board [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The choice of referred RPR is imperative in assessing the emission inventory, as they vary considerably among references.\u003c/p\u003e \u003cp\u003eEspecially in the case of rice residues, the value of RPR varied by rice variety, water supply, and harvesting method [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and might refer to rice stubble as rice straw in general. These are often considered as equivalent.\u003c/p\u003e \u003cp\u003eLocal references present the RPR of dry-weight rice straw as 0.5 [30] and 0.81 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The study's survey [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e32\u003c/span\u003e] reported the production of rice straw and rice stubble as 357 (58%) and 262 (42%) kg per rai or about 0.74 and 0.54 RPR, respectively, for the rice production of around 3.1 ton per ha [27]. Similarly, the result of 650 kg per rai or about 1.4 RPR of rice straw and stubble together was also found [33]. The study [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e29\u003c/span\u003e] estimated rice straw\u0026rsquo;s RPR across 15 rice varieties by collecting samples, with results ranging between 1 and 1.4 or 3.1 and 4.6 ton per ha, accounting for 60% of the total residue. Studies in China cited rice straw\u0026rsquo;s RPR as 0.9 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and 0.93\u0026ndash;1.28 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A foreign study in Southeast Asia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e35\u003c/span\u003e] reports 0.5, 0.7, and 1.4 RPR of rice straw, depending on the cutting height of the stubble (40, 20, and 0 cm respectively). Accordingly, this study applied an average RPR of roughly 0.8 for rice straw from local studies, and that of rice stubble was estimated to be 40% of the total residue, or around 0.5.\u003c/p\u003e \u003cp\u003eThe cited maize\u0026rsquo;s RPR of 1 was also an average of study values. The official source [30] presented a value of 1.84 with 42% moisture content or about 1.2 on a dry-weight basis. Local studies used values of 0.82 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and 0.89 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e37\u003c/span\u003e] on a dry-weight basis with about 10% moisture content. The study\u0026rsquo;s survey of energy potential from maize residue presented as 1.1 RPR [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Local references report the sugarcane\u0026rsquo;s RPR of 0.2 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and 0.17 [30] on a dry-weight basis. Some studies reported values of 0.28 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and 0.24\u0026ndash;0.44 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e40\u003c/span\u003e], with about 1 and 0.8 kg per m\u003csup\u003e2\u003c/sup\u003e biomass loads respectively. However, according to an average sugarcane production of around 9\u0026ndash;12 ton per rai [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the RPR of these two studies should be 0.15\u0026ndash;0.17 based on the biomass load. Therefore, a sugarcane\u0026rsquo;s RPR of 0.17 was used. The fraction of dry matter followed the sources of dry-weight RPR which present around 10% of moisture content. A constant value of burn efficiency was used [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The emission factors for the three mentioned GHGs were mostly derived from local literature.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Coefficients and crops used for the estimation of GHG emission from crop residue burning\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients and crops used for the estimation of GHG emission from crop residue burning\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidue-to-product ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDry mater fraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBurn\u003c/p\u003e \u003cp\u003eEfficiency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eEmission factor (g CO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,177\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.51\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,350\u003csup\u003ec,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.4\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugarcane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,153\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e41\u003c/span\u003e], \u003csup\u003eb\u003c/sup\u003e [42], \u003csup\u003ec\u003c/sup\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], \u003csup\u003ed\u003c/sup\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e43\u003c/span\u003e], \u003csup\u003ee\u003c/sup\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe burned areas specific to each of crop, derived from the MODIS data, along with the gathered data, are key variables used to estimate the GHG emissions from CRB using the following Eq.\u0026nbsp;(1):\u003c/p\u003e \u003cp\u003e \u003cem\u003eE\u003c/em\u003e \u003csub\u003e \u003cem\u003eig\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e= R\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026times; P\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026times; A\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026times; F\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026times; B\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026times; EF\u003c/em\u003e\u003csub\u003e\u003cem\u003eig\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026times; p\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e (1)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eE\u003c/em\u003e is the GHG emission (g CO\u003csub\u003e2\u003c/sub\u003ee); \u003cem\u003ei\u003c/em\u003e is the crop type (rice, corn, or sugarcane); \u003cem\u003eg\u003c/em\u003e is GHG type (CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e or N\u003csub\u003e2\u003c/sub\u003eO); \u003cem\u003eR\u003c/em\u003e is the residue-to-product ratio; \u003cem\u003eP\u003c/em\u003e represents crop production (kg per rai); \u003cem\u003eA\u003c/em\u003e is the burned area (rai); \u003cem\u003eF\u003c/em\u003e is the fraction of dry matter ; \u003cem\u003eB\u003c/em\u003e is the burn efficiency; and \u003cem\u003eEF\u003c/em\u003e is the emission factor (g CO\u003csub\u003e2\u003c/sub\u003ee kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). \u003cem\u003ep represents\u003c/em\u003e global warming potential of the different GHG type (g CO\u003csub\u003e2\u003c/sub\u003ee g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Finally, the sum of the three GHG types is the total GHG emissions of different crops in units of g CO\u003csub\u003e2\u003c/sub\u003ee.\u003c/p\u003e \u003cp\u003eAlthough calculating the GHG emissions from the open-burning of crop residues is a key objective of this study, the indirect GHG emissions for nutrient losses were also assessed. Since crop residue is typically mulched, retained or incorporated into the field, without burning or further utilization (such as power generation or briquette production), its nutrients replace the proportion of the chemical fertilizer required. It is important to note that while mulching and soil incorporation may contribute to GHG emissions, their impact is generally insignificant, and tillage is typically a pre-cultivated practice.\u003c/p\u003e \u003cp\u003eFor the calculation, this indirect effect is simply the multiplication of the amount of crop residue, the nutrient content acquired from the local study [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and emission factor of chemical fertilizer manufacturing published by the Thailand Greenhouse Gas Management Organization [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor data gathering, CO\u003csub\u003e2\u003c/sub\u003e emission coefficients or emission factors were gathered from the Thailand Greenhouse Gas Management Organization (TGO) and the Intergovernmental Panel on Climate Change (IPCC). The GHG emissions were assessed in terms of carbon dioxide equivalents (CO\u003csub\u003e2\u003c/sub\u003ee), considering different agricultural residue management methods. CO\u003csub\u003e2\u003c/sub\u003ee represents the primary greenhouse gas emission metric used to evaluate the climate impacts of various practices. Key data for this analysis included:\u003c/p\u003e \u003cp\u003eFor activities where non-CO\u003csub\u003e2\u003c/sub\u003e greenhouse gases, such as methane (CH\u003csub\u003e4\u003c/sub\u003e) or sulfur hexafluoride (SF\u003csub\u003e6\u003c/sub\u003e), were emitted during agricultural residue management, the calculated values were converted into CO\u003csub\u003e2\u003c/sub\u003ee using the Global Warming Potential (GWP) factors for each gas, as specified by the IPCC. The GWP values used for CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4,\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO, were 1, 21 and 310 respectively [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter estimating CO\u003csub\u003e2\u003c/sub\u003ee for each activity, the economic value of the associated environmental impact was determined by converting the CO\u003csub\u003e2\u003c/sub\u003ee emissions using the carbon credit concept. This was done using the ICAP Allowance Price Explorer, with the European Union Emission Trading Scheme (EU ETS) selected as the reference due to its status as the world's largest carbon market [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Social Cost of Carbon\u003c/h2\u003e \u003cp\u003eThis study assesses the Social Cost of Carbon (SCC) associated with GHG emissions from agricultural residues in Thailand. The SCC, a measure reflecting the economic cost of emitting one ton of CO₂e, is calculated by multiplying the carbon emissions (measured in tons of CO₂ equivalents, or ton CO₂e) by the carbon credit price in Thailand, which averaged 34.34 baht per ton CO₂e [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The emissions data for rice, maize, and sugarcane residues across five Regions of Thailand (North, North-East, Central, East, and South) were derived from carbon accounting methodologies that measure the total CO₂e emissions for each crop type, province and region. The study quantifies the economic burden or \"cost\" of emissions by crop and region, facilitating an understanding of the regional and crop-specific carbon impacts within Thailand\u0026rsquo;s agricultural sector.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eThe results of this study demonstrate the significant extent of open burning in agricultural areas across Thailand. Annually, approximately 551,000 rai of rice fields, 175,000 rai of maize fields, and 156,000 rai of sugarcane fields are subjected to open burning practices. These activities contribute to substantial greenhouse gas emissions, with rice field burning releasing an estimated 473,554 tons of CO\u003csub\u003e2\u003c/sub\u003ee, maize fields contributing 223,790 tons, and sugarcane fields adding 360,355 tons.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Agricultural Residue in Thailand's Agricultural Areas\u003c/h2\u003e \u003cp\u003eStudies on agricultural residue burning in Thailand show that the highest levels of burning are concentrated in the Northern Region, primarily in rice and maize fields, while sugarcane burning is most prevalent in the Central Region. The following sections provide a detailed analysis by crop.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Rice Cultivation and Residue Burning\u003c/h2\u003e \u003cp\u003eRice cultivation in Thailand spans a total area of 68\u0026nbsp;million rai, predominantly in the North-Eastern Region (60%), followed by the Northern (23%) and Central (12%) Regions. Despite having a smaller cultivation area than the North-Eastern Region, the Northern Region experiences the highest incidence of rice residue burning, accounting for 42% (230,914 rai) of the total 551,727 rai burned annually (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is followed by the Central Region (33%), while no burning is reported in the Southern Region. The provinces most affected by rice field burning include Uttaradit (94,484 rai), Suphan Buri (68,263 rai), and Phra Nakhon Si Ayutthaya (41,481 rai), which together account for significant proportions of the total burned area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis pattern is largely attributed to the off-season rice cultivation prevalent in the Northern and Central Regions, where farmers often grow multiple crops annually, necessitating rapid residue removal between planting cycles [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Approximately 45% of farmers resort to burning as a cost-effectiveness method for clearing fields before and after the harvest, despite the negative environmental impacts. Additionally, 60% of the estimated 117\u0026nbsp;million tons of rice residues are burned, although only 15% are fully burned due to soil moisture levels, leading to incomplete combustion [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Such practices contribute significantly to air pollution and related health risks, as discussed by Thailand Climate Change Network, which highlights the adverse effects of particulate matter (PM) from rice residue burning on both environmental quality and public health.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Maize Cultivation and Residue Burning\u003c/h2\u003e \u003cp\u003eMaize is cultivated on 9.67\u0026nbsp;million rai in Thailand, with the Northern Region accounting for the largest share of both cultivation and burning. Of the total maize fields burned, 74.58% (130,521 rai) are located in the Northern Region, followed by the Central (15.19%) and North-Eastern (9.94%) Regions respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The provinces with the highest levels of maize burning include Tak (45,694 rai), Phetchabun (30,996 rai), and Kanchanaburi (15,804 rai).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMaize is particularly well-suited to the Northern highlands due to its low water requirements and resilience to sloped terrain. However, the challenges of residue management in these highland areas, especially during the dry season (January to April), lead many farmers to burn crop residues. This practice is favored for its efficiency in controlling weeds and pests, reducing the reliance on chemical inputs, but it exacerbates air quality issues in the Region. According to Thai Publica (2012) and Green News (2021) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], maize residue burning is a major contributor to seasonal haze and the associated respiratory problems in Northern Thailand. In contrast, the North-Eastern and Central Regions experience significantly lower levels of maize burning, as seen in provinces such as Loei, Nakhon Ratchasima, and Lopburi, which are characterized by less intensive maize farming practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Sugarcane Cultivation and Residue Burning\u003c/h2\u003e \u003cp\u003eSugarcane cultivation in Thailand covers 19.25\u0026nbsp;million rai, with the North-Eastern Region accounting for 49% of the total area, followed by the Central (24%) and Northern (22%) Regions. Of the total 156,362 rai of sugarcane fields burned annually, the Central Region is the most affected, with 50.74% of the burned area, followed by the Northern (19.25%) and North-Eastern (18.7%) Regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The provinces with the highest levels of sugarcane burning are Suphan Buri (31,959 rai), Lopburi (31,465 rai), and Sa Kaeo (12,684 rai).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSugarcane burning typically occurs in three stages: pre-harvest, post-harvest, and pre-planting. Pre-harvest burning is the most common, primarily driven by labor shortages and the need to reduce harvesting time. However, this practice reduces both the weight and quality of the harvested cane, negatively affecting the overall profitability of the crop [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The expansion of sugarcane cultivation in Thailand has led to increased burning, especially in the Central Region, where large-scale commercial farming predominates.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Greenhouse Gas Emissions from Agricultural residuals\u003c/h2\u003e \u003cp\u003eThe analysis of GHG emissions from agricultural residues of rice, maize, and sugarcane in Thailand reveals substantial regional and crop-specific differences. The emissions from these residues are measured in ton CO₂e, highlighting how each crop and its respective geographic distribution contribute variously contribute to Thailand's overall agricultural GHG profile (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon Equivalent Emission from rice, maize and sugarcane residuals (unit: ton CO₂e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProvince\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eSugarcane\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost of Carbon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eEmission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCost of Carbon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c9\" namest=\"c8\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eEmission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCost of Carbon\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(ton CO₂ e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Thai Baht)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e(ton CO₂ e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(Thai Baht)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e(ton CO₂e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(Thai Baht)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e151,604\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e27,743,532\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNorth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e119,980\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e21,956,340\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eNorth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e53,428\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e9,777,324\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUttaradit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,352,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e42,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,686,732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePhetchabun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e20,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,799,263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Sawan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,453,854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhetchabun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e28,493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,214,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNakhon Sawan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e10,606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,940,898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhitsanulok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,476,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMae Hong Son\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,944,192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eKamphaeng Phet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5,943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,087,569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhichit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,301,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChiang Mai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9,370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,714,710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUttaradit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5,697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,042,551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhrae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,983,720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhrae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,469,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePhitsanulok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,016,748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKamphaeng Phet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,110,627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhitsanulok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5,442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e995,886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSukhothai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3,315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e606,645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhetchabun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e849,120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e702,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eTak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e159,759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLampang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e684,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNakhon Sawan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e655,872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePhichit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e91,134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChiang Rai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e524,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUttaradit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3,356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e614,148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eLampang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32,757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSukhothai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLamphun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e374,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMae Hong Son\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLampang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e341,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChiang Rai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e142,923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhayao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChiang Mai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114,375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKamphaeng Phet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14,091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhayao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhichit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLamphun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSukhothai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth-East\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e78,291\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14,327,253\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNorth-East\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e15,983\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2,924,889\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eNorth-East\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e51,802\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e9,479,766\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKalasin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,746,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e12,318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,254,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eChaiyaphum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e18,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,464,007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoi Et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,685,708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNakhon- Ratchasima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e655,140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNakhon- Ratchasima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e10,753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,967,799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSakon-\u003c/p\u003e \u003cp\u003eNakhon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,705,926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNong Bua Lam Phu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eKhon Kaen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,029,192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhon Kaen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,650,111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChaiyaphum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUdon Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5,338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e976,854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUdon Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e690,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKhon Kaen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eKalasin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3,460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e633,180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Phanom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e658,068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNong Khaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNong Bua Lam Phu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2,447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e447,801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBueng Kan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther \u003c/p\u003e \u003cp\u003eprovinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRoi Et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e400,770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaha -\u003c/p\u003e \u003cp\u003eSarakham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e601,704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSakon-\u003c/p\u003e \u003cp\u003eNakhon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e212,646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChaiyaphum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e552,477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMaha- \u003c/p\u003e \u003cp\u003eSarakham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e141,825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYasothon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e421,998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eLoei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e109,617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon- Ratchasima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e366,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNong- Khaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e33,489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNong Khaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267,363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eAmnat- Charoen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32,757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUbon Rat-\u003c/p\u003e \u003cp\u003echathani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249,429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eBuri Ram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23,973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236,985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNakhon Phanom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3,843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNong Bua Lam Phu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184,647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eYasothon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmnat Charoen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183,915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi Sa Ket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuri Ram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMukdahan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e118,522\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e21,689,526\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCentral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e24,436\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4,471,788\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCentral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e140,717\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e25,751,211\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuphan Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,201,511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKanchanaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14,528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,658,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSuphan Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e56,681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10,372,623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAyutthaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,983,822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLop Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,283,745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eLop Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e55,804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10,212,132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChai Nat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,838,879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUthai Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e377,346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eKanchanaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e12,583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,302,689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLop Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,626,599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaraburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e114,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUthai Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6,163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,127,829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Nayok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,086,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuphan Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17,751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRatchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e773,175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Pathom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNakhon Pathom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e408,273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSing Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChai Nat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eChai Nat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e231,129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaraburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110,715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhetchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePhetchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e167,079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonthaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSaraburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e101,016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathum Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSing Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e55,266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAng Thong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBangkok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUthai Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhetchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanchanaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e13,813\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2,527,779\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e478\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e87,474\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eEast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e31,367\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e5,740,161\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrachin Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,289,967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa Kaeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80,337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSa Kaeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e22,496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4,116,768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChachoengsao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e984,723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChanthaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePrachin Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5,679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,039,257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSa Kaeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrachin Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eChon Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e439,749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanthaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eChachoengsao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e144,387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChon Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRayong and Trat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(All provinces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSouth\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(All provinces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSouth\u003c/b\u003e\u003c/p\u003e \u003cp\u003e (All provinces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Northern Region records the highest emissions from rice residues at 151,604 ton CO₂e, with Uttaradit (62,033 ton CO₂e) and Nakhon Sawan (24,338 ton CO₂e) as major contributors. In the North-Eastern, emissions are comparatively lower, totaling 78,292 ton CO₂e, led by Kalasin and Roi Et. The Central Region ranks second with 118,552 ton CO₂e, primarily from Suphan Buri and Ayutthaya. These results indicate that rice residue emissions are most pronounced in the Northern, likely due to larger cultivation areas and practices favoring residue burning.\u003c/p\u003e \u003cp\u003eMaize residue emissions are highest in the Northern Region, with a total of 119,980 ton CO₂e, dominated by Tak and Phetchabun. The North-Eastern contributes only 15,982 ton CO₂e, led by Loei, while the Central Region's maize residue emissions total 24,435 ton CO₂e, mainly from Kanchanaburi and Lop Buri. This pattern suggests that the Northern Region's maize cultivation practices are significantly more emissions-intensive than those in other Regions.\u003c/p\u003e \u003cp\u003eUnlike rice and maize, sugarcane residue emissions are highest in the Central Region, reaching 140,716 ton CO₂e, with Suphan Buri and Lop Buri as primary sources. The Northern and North-Eastern Regions produce 53,428 ton CO₂e and 51,803 ton CO₂e, respectively, with Phetchabun and Chaiyaphum as leading emitters. This data underscores that Central Thailand's sugarcane cultivation is highly emissions-intensive due to prevalent residue-burning practices in this Region.\u003c/p\u003e \u003cp\u003eGHG emissions from agricultural residues in Thailand are heavily influenced by both regional practices and crop types. The Northern Region, with intensive rice and maize cultivation, shows the highest emissions, primarily due to prevalent open burning methods for residue management. In contrast, the North-Eastern Region, despite cultivating rice, exhibits lower emissions, possibly due to less frequent residue burning. The Central Region, dominated by sugarcane, also has significant emissions from open burning, highlighting the potential impact of a transition to bio-energy or other non-burning practices. These findings underscore the importance of crop- and region-specific strategies for reducing agricultural emissions in Thailand.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Carbon Equivalent Emission from rice, maize and sugarcane residuals (unit: ton CO₂e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Social Cost of Carbon\u003c/h2\u003e \u003cp\u003eThe total SCC for emissions from rice residues was approximately 66\u0026nbsp;million baht, with the highest SCC in the Northern Region (27,743,607 baht), due to the large scale of rice farming and emissions from open burning practices. For maize, the total SCC was around 29\u0026nbsp;million baht, with the Northern Region again contributing the largest share (21,956,266 baht) because of intensive cultivation practices. Sugarcane emissions resulted in a total SCC of about 51\u0026nbsp;million baht, with the Central Region bearing the highest cost (25,751,048 baht), reflecting the prevalence of sugarcane farming in that area and associated residue burning practices. The details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost of Carbon from rice, maize and sugarcane residuals (unit: Thai Baht)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProvince\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProvince\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSugarcane\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,743,532\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,956,340\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,777,324\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUttaradit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,352,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,686,732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhetchabun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,799,263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Sawan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,453,854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhetchabun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,214,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNakhon Sawan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,940,898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhitsanulok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,476,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMae Hong Son\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,944,192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKamphaeng Phet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,087,569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhichit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,301,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChiang Mai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,714,710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUttaradit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,042,551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhrae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,983,720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhrae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,469,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhitsanulok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,016,748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKamphaeng Phet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,110,627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhitsanulok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e995,886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSukhothai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e606,645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhetchabun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e849,120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e702,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e159,759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLampang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e684,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNakhon Sawan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e655,872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhichit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91,134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChiang Rai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e524,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUttaradit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e614,148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLampang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32,757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSukhothai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLamphun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMae Hong Son\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e253,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLampang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChiang Rai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142,923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhayao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChiang Mai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114,375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKamphaeng Phet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhayao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhichit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLamphun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSukhothai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth-East\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14,327,253\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNorth-East\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2,924,889\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNorth-East\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e9,479,766\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKalasin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,746,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,254,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChaiyaphum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,464,007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoi Et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,685,708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNakhon Ratchasima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e655,140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNakhon Ratchasima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,967,799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSakon Nakhon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,705,926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNong Bua Lam Phu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKhon Kaen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,029,192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhon Kaen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,650,111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChaiyaphum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUdon Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e976,854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUdon Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e690,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKhon Kaen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKalasin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e633,180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Phanom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e658,068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNong Khaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNong Bua Lam Phu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e447,801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBueng Kan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40,077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRoi Et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e400,770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaha Sarakham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e601,704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSakon Nakhon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e212,646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChaiyaphum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e552,477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaha Sarakham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141,825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYasothon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421,998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLoei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109,617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Ratchasima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNong Khaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33,489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNong Khaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267,363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAmnat Charoen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32,757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUbon Ratchathani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249,429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuri Ram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23,973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236,985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNakhon Phanom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNong Bua Lam Phu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184,647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYasothon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmnat Charoen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183,915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi Sa Ket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30,195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuri Ram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMukdahan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e21,689,526\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCentral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4,471,788\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCentral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e25,751,211\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuphan Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,201,511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKanchanaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,658,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSuphan Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,372,623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAyutthaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,983,822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLop Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,283,745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLop Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,212,132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChai Nat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,838,879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUthai Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377,346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKanchanaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,302,689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLop Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,626,599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaraburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUthai Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,127,829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Nayok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,086,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuphan Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRatchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e773,175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNakhon Pathom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e287,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRatchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNakhon Pathom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e408,273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSing Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChai Nat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChai Nat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e231,129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaraburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110,715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhetchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhetchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e167,079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonthaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaraburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101,016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathum Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSing Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55,266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAng Thong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBangkok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUthai Thani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31,842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhetchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanchanaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25,986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatchaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2,527,779\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e87,474\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5,740,161\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrachin Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,289,967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSa Kaeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80,337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSa Kaeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,116,768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChachoengsao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e984,723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChanthaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrachin Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,039,257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSa Kaeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrachin Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChon Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e439,749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanthaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChachoengsao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144,387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChon Buri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRayong and Trat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(All provinces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSouth \u003c/b\u003e\u003c/p\u003e \u003cp\u003e(All provinces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSouth\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(All provinces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Cost of Carbon from rice, maize and sugarcane residuals (unit: Thai Baht)\u003c/p\u003e\u003cp\u003eThe SCC findings highlight substantial social costs attributed to regional agricultural practices, with rice and maize in the Northern and sugarcane in the Central Regions as the largest contributors to carbon costs. These insights underscore the economic implications of residue management practices and point to potential cost savings through sustainable residue management alternatives, particularly in high-emission areas.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Summary of Cultivated and Burned Areas of Rice, Maize, and Sugarcane: Greenhouse Gas Emissions and Social Cost of Carbon from Crop Residues across the Regions\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Cultivated and Burned Areas of Rice, Maize, and Sugarcane: Greenhouse Gas Emissions and Social Cost of Carbon from Crop Residues across the Regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivated Area \u003c/p\u003e \u003cp\u003e(Rai)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBurned Area (Rai)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarbon Emission \u003c/p\u003e \u003cp\u003e(ton CO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSocial Cost of Carbon \u003c/p\u003e \u003cp\u003e(Thai Baht)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,360,965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230,914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151,604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,743,607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40,939,421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78,292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,327,400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,282,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180,525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118,522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21,689,523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,304,528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,527,771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,204,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e68,092,293\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e551,727\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e362,231\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e66,288,301\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaize\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,586,619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130,521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119,980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21,956,266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,362,818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,924,676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e680,124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24,435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,471,629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87,474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9,673,822\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e175,009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e160,875\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e29,440,045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSugarcane\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,243,030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53,428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,777,297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,467,956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,480,003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,630,232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79,342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140,716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,751,048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e905,607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,740,125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e19,247,705\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e156,362\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e277,314\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e50,748,473\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThailand \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ein Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e97,013,820\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e883,098\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e800,420\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e146,476,820\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis comprehensive analysis presents (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) cultivated and burned areas for rice, maize, and sugarcane across different Regions of Thailand, detailing their associated carbon emissions and the social costs of carbon. The data reveals that rice cultivation occupies the largest area (68,092,293 rai), with 551,727 rai burned, generating 362,231 ton CO\u003csub\u003e2\u003c/sub\u003ee and incurring a social carbon cost of 66,288,301 Thai Baht. Across all three crops, Thailand's total cultivated area reaches 97,013,820 rai, with 883,098 rai burned, producing 800,420 ton CO\u003csub\u003e2\u003c/sub\u003ee and a total social carbon cost of 146,476,820 Thai Baht.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Policy","content":"\u003cp\u003eAgricultural residue burning is a significant contributor to GHG emissions globally, yet detailed, region-specific analyses are often lacking, particularly in Southeast Asia. Previous studies have extensively documented the environmental and economic impacts of open burning [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, there remains a knowledge gap concerning the integration of advanced remote sensing technologies with detailed emissions inventories and economic assessments, particularly in Thailand's diverse agricultural landscapes. This study bridges these gaps by providing a comprehensive analysis of burned areas, GHG emissions, and associated social costs for rice, maize, and sugarcane residues.\u003c/p\u003e \u003cp\u003eThe findings indicate substantial environmental impacts from open burning, with total emissions of approximately 800,000 tons CO₂e annually, primarily concentrated in rice (473,554 tons CO₂e), maize (223,790 tons CO₂e), and sugarcane (360,355 tons CO₂e). Spatial analysis revealed the Northern Region as the hotspot for rice and maize burning, while the Central Region exhibited the highest prevalence of sugarcane burning. These emissions translate to a significant economic burden, with a calculated SCC of approximately 146\u0026nbsp;million baht annually. This spatial analysis could serve as a basis for targeting pilot areas for policy-specific measurement.\u003c/p\u003e \u003cp\u003eThe emission patterns observed align with studies in China and India, where crop residue burning remains a prevalent practice due to its low cost and efficiency [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the emissions per hectare in Thailand are relatively higher, potentially due to differences in residue-to-product ratios and combustion efficiencies. The SCC findings also underscore the disproportionate burden borne by specific regions, emphasizing the need for regionally targeted interventions. Similar trends were observed in India, where SCC varied widely across states [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. However, the study also highlights Thailand's potential to lead in adopting innovative residue management techniques, building on regional research in bio-energy and carbon sequestration [19].\u003c/p\u003e \u003cp\u003eChallenges in enforcing residue management policies stem from limited financial support and awareness among farmers. Addressing these gaps requires targeted education and subsidy programs to incentivize sustainable practices. Policymakers should prioritize funding for localized pilot projects on biochar and composting, coupled with assessments of their long-term environmental and economic benefits. Establishing carbon markets specific to agricultural residues could also drive innovation and investment. A multi-faceted approach combining regulatory enforcement, economic incentives, and farmer education is essential to transition Thailand's agricultural sector towards sustainability. Collaboration between government agencies, researchers, and farming communities will be critical in achieving these goals.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study underscores the critical environmental challenges posed by open burning practices in Thailand's rice, maize, and sugarcane cultivation areas, particularly in the Northern and Central Regions. The substantial greenhouse gas emissions linked to these practices, estimated at approximately one million tons CO\u003csub\u003e2\u003c/sub\u003ee annually, highlight the urgent need for effective mitigation strategies. The analysis reveals that open-burning, while prevalent due to its cost-effectiveness, significantly undermines the nation's climate objectives and public health. Therefore, it is imperative to implement regionally targeted policy measures that promote sustainable alternatives to open burning, such as composting and biomass utilization. By focusing on high-emission crops and areas, policymakers can foster a transition towards environmentally friendly agricultural practices, enhancing air quality and contributing to Thailand's broader climate goals. Collaborative efforts among government agencies, researchers, and farming communities will be essential in driving these changes and achieving meaningful reductions in agricultural emissions. The findings of this study not only call for immediate action but also provide a roadmap for sustainable agricultural management that aligns with ecological and economic sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are\u0026nbsp;available on a request\u0026nbsp;from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Thailand Research Fund under grant number N21A650033 through the Office of Agricultural Policy Research, Knowledge Network Institute of Thailand.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYingluck Kanchanaroek led the study\u0026apos;s conceptualization and coordinated the project administration. She conducted the social cost analysis and played a central role in original draft manuscript writing and editing. Totsanat Rattanakaew was responsible for the agricultural land use and burned area analysis, spatial mapping, and editing of the Spatial methodology section. Pidok Kako conducted the greenhouse gas emission analysis and edited related sections of the manuscript. Onicha Meangbua curated social cost data, managed the references, and handled formatting. Warayut Doungjun was responsible for data visualization and agricultural residue data curation. All authors contributed to framing, reviewing, and finalizing the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003eThis paper involves secondary analysis of agricultural and environmental data and does not involve human or animal subjects. While we utilized some spatial data from a previously approved research project (Ethics Review Committee for Human Research, Thammasat University (Social Science), Research Project Code 012/2565, Certificate of Approval No. 007/2565), the current paper represents a substantial extension and further development of that original work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the College of Interdisciplinary Studies, Thammasat University\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eEvenson, R., \u0026amp; Gollin, D. Assessing the Impact of the Green Revolution, 1960 to 2000. Science, 2003;758-762.\u003c/li\u003e\n \u003cli\u003eOffice of Agricultural Economics (OAE). Area of agricultural use. 2021. https://www.oae.go.th/view/1/ตารางแสดงรายละเอียดข้าวนาปี/TH-TH. 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Science of The Total Environment. 2023;904:1-14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sustainable-environment-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sere","sideBox":"Learn more about [Sustainable Environment Research](https://sustainenvironres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sere/default.aspx","title":"Sustainable Environment Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"GHG, Open-burning, agricultural waste, and Social Cost of Carbon","lastPublishedDoi":"10.21203/rs.3.rs-5924571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5924571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the environmental and economic impacts of agricultural residue burning in Thailand, focusing on rice, maize, and sugarcane, which collectively occupy 96.8\u0026nbsp;million Rai annually and generate 114\u0026nbsp;million tons of residues. Open burning is a cost-effective but environmentally detrimental practice that contributes significantly to greenhouse gas (GHG) emissions. This research aims to quantify the burned areas, estimate GHG emissions, and assess the Social Cost of Carbon (SCC) using Geographic Information System (GIS) techniques and MODIS satellite imagery combined with bottom-up approach emissions calculations. In addition, the cost of carbon emissions was estimated using the average carbon credit price in Asia as a representative benchmark. The findings reveal annual GHG emissions of approximately 800,000 tons CO\u003csub\u003e₂\u003c/sub\u003ee, primarily from rice (362,231 tons), maize (160,875 tons), and sugarcane (277,314 tons). The SCC is estimated at 146\u0026nbsp;million Baht, disproportionately affecting the Northern and Central Regions, which exhibit the highest prevalence of burning for rice sugarcane and maize, respectively. This spatial analysis highlights key hot-spots and provides critical insights to inform targeted policy interventions. Its findings emphasize the need for regionally tailored policies to mitigate the environmental and economic costs of open burning. Sustainable alternatives, such as composting are recommended, supported by targeted education, financial incentives, and policy measures. These strategies could substantially reduce emissions, improve air quality, and align Thailand\u0026rsquo;s agricultural sector with its climate and sustainability goals.\u003c/p\u003e","manuscriptTitle":"Spatial-economic analysis of greenhouse gas emissions from agricultural residue burning in Thailand’s rice, maize, and sugarcane cultivation areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 18:15:21","doi":"10.21203/rs.3.rs-5924571/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-20T05:55:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-19T00:38:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-17T09:52:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sustainable Environment Research","date":"2025-03-16T03:06:22+00:00","index":"","fulltext":""},{"type":"decision","content":"Major revision","date":"2025-03-14T02:25:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sustainable-environment-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sere","sideBox":"Learn more about [Sustainable Environment Research](https://sustainenvironres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sere/default.aspx","title":"Sustainable Environment Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8296834-2f68-4f94-af4a-913d27db1a6e","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:10:26+00:00","versionOfRecord":{"articleIdentity":"rs-5924571","link":"https://doi.org/10.1186/s42834-025-00261-1","journal":{"identity":"sustainable-environment-research","isVorOnly":false,"title":"Sustainable Environment Research"},"publishedOn":"2025-10-14 15:57:19","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-03-20 18:15:21","video":"","vorDoi":"10.1186/s42834-025-00261-1","vorDoiUrl":"https://doi.org/10.1186/s42834-025-00261-1","workflowStages":[]},"version":"v1","identity":"rs-5924571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5924571","identity":"rs-5924571","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}